MaOAOA: A Novel Many‐Objective Arithmetic Optimization Algorithm for Solving Engineering Problems

ABSTRACT Currently, the use of multi‐objective optimization algorithms has been applied in many fields to find the efficient solution of the multiple objective optimization problems (MOPs). However, this reduces their efficiency when addressing MaOPs, which are problems that contain more than three...

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Published in:Engineering reports (Hoboken, N.J.) Vol. 7; no. 3
Main Authors: Jangir, Pradeep, Arpita, Pandya, Sundaram B., G., Gulothungan, Khishe, Mohammad, Trivedi, Bhargavi Indrajit
Format: Journal Article
Language:English
Published: Hoboken, USA John Wiley & Sons, Inc 01.03.2025
Wiley
Subjects:
Algorithms
Arithmetic
Convergence
Decision making
Decomposition
Genetic algorithms
information feedback mechanism
many‐objective arithmetic optimization algorithm
many‐objective optimization
Mathematical analysis
metaheuristic algorithm
Multiple objective analysis
Objectives
Optimization
Pareto optimality
Pareto optimum
Variables
ISSN:2577-8196, 2577-8196
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Abstract ABSTRACT Currently, the use of multi‐objective optimization algorithms has been applied in many fields to find the efficient solution of the multiple objective optimization problems (MOPs). However, this reduces their efficiency when addressing MaOPs, which are problems that contain more than three objectives; this is because the portion of the Pareto frontier solutions tends to increase exponentially with the number of objectives. This paper aims at overcoming this problem by proposing a new Many‐Objective Arithmetic Optimization Algorithm (MaOAOA) that incorporates a reference point, niche preservation, and an information feedback mechanism (IFM). They did this in a manner that splits the convergence and the diversity phases in the middle of the cycle. The first phase deals with the convergence using a reference point approach, which aims to move the population towards the true Pareto Front. However, the diversity phase of the MaOAOA uses a niche preserve to the archive truncation method in the population, thus guaranteeing that the population is spread out properly along the actual Pareto front. These stages are mutual; that is, the convergence stage supports the diversity stage, and they are balanced by an (IFM) approach. The experimental results show that MaOAOA outperforms several approaches, including MaOTLBO, NSGA‐III, MaOPSO, and MOEA/D‐DRW, in terms of GD, IGD, SP, SD, HV, and RT metrics. This can be seen from the MaF1‐MaF15 test problems, especially with four, seven, and nine objectives, and five real‐world problems that include RWMaOP1 to RWMaOP5. The findings indicate that MaOAOA outperforms the other algorithms in most of the test cases analyzed in this study. MaOAOA introduces an innovative many‐objective optimization framework combining an Information Feedback Mechanism, reference point‐based selection, and niche preservation strategies. Its effectiveness surpasses leading algorithms in convergence, diversity, and computational efficiency across benchmark and real‐world engineering problems, establishing it as a robust solution for high‐dimensional optimization challenges.
AbstractList ABSTRACT Currently, the use of multi‐objective optimization algorithms has been applied in many fields to find the efficient solution of the multiple objective optimization problems (MOPs). However, this reduces their efficiency when addressing MaOPs, which are problems that contain more than three objectives; this is because the portion of the Pareto frontier solutions tends to increase exponentially with the number of objectives. This paper aims at overcoming this problem by proposing a new Many‐Objective Arithmetic Optimization Algorithm (MaOAOA) that incorporates a reference point, niche preservation, and an information feedback mechanism (IFM). They did this in a manner that splits the convergence and the diversity phases in the middle of the cycle. The first phase deals with the convergence using a reference point approach, which aims to move the population towards the true Pareto Front. However, the diversity phase of the MaOAOA uses a niche preserve to the archive truncation method in the population, thus guaranteeing that the population is spread out properly along the actual Pareto front. These stages are mutual; that is, the convergence stage supports the diversity stage, and they are balanced by an (IFM) approach. The experimental results show that MaOAOA outperforms several approaches, including MaOTLBO, NSGA‐III, MaOPSO, and MOEA/D‐DRW, in terms of GD, IGD, SP, SD, HV, and RT metrics. This can be seen from the MaF1‐MaF15 test problems, especially with four, seven, and nine objectives, and five real‐world problems that include RWMaOP1 to RWMaOP5. The findings indicate that MaOAOA outperforms the other algorithms in most of the test cases analyzed in this study. MaOAOA introduces an innovative many‐objective optimization framework combining an Information Feedback Mechanism, reference point‐based selection, and niche preservation strategies. Its effectiveness surpasses leading algorithms in convergence, diversity, and computational efficiency across benchmark and real‐world engineering problems, establishing it as a robust solution for high‐dimensional optimization challenges.
Currently, the use of multi‐objective optimization algorithms has been applied in many fields to find the efficient solution of the multiple objective optimization problems (MOPs). However, this reduces their efficiency when addressing MaOPs, which are problems that contain more than three objectives; this is because the portion of the Pareto frontier solutions tends to increase exponentially with the number of objectives. This paper aims at overcoming this problem by proposing a new Many‐Objective Arithmetic Optimization Algorithm (MaOAOA) that incorporates a reference point, niche preservation, and an information feedback mechanism (IFM). They did this in a manner that splits the convergence and the diversity phases in the middle of the cycle. The first phase deals with the convergence using a reference point approach, which aims to move the population towards the true Pareto Front. However, the diversity phase of the MaOAOA uses a niche preserve to the archive truncation method in the population, thus guaranteeing that the population is spread out properly along the actual Pareto front. These stages are mutual; that is, the convergence stage supports the diversity stage, and they are balanced by an (IFM) approach. The experimental results show that MaOAOA outperforms several approaches, including MaOTLBO, NSGA‐III, MaOPSO, and MOEA/D‐DRW, in terms of GD, IGD, SP, SD, HV, and RT metrics. This can be seen from the MaF1‐MaF15 test problems, especially with four, seven, and nine objectives, and five real‐world problems that include RWMaOP1 to RWMaOP5. The findings indicate that MaOAOA outperforms the other algorithms in most of the test cases analyzed in this study.
ABSTRACT Currently, the use of multi‐objective optimization algorithms has been applied in many fields to find the efficient solution of the multiple objective optimization problems (MOPs). However, this reduces their efficiency when addressing MaOPs, which are problems that contain more than three objectives; this is because the portion of the Pareto frontier solutions tends to increase exponentially with the number of objectives. This paper aims at overcoming this problem by proposing a new Many‐Objective Arithmetic Optimization Algorithm (MaOAOA) that incorporates a reference point, niche preservation, and an information feedback mechanism (IFM). They did this in a manner that splits the convergence and the diversity phases in the middle of the cycle. The first phase deals with the convergence using a reference point approach, which aims to move the population towards the true Pareto Front. However, the diversity phase of the MaOAOA uses a niche preserve to the archive truncation method in the population, thus guaranteeing that the population is spread out properly along the actual Pareto front. These stages are mutual; that is, the convergence stage supports the diversity stage, and they are balanced by an (IFM) approach. The experimental results show that MaOAOA outperforms several approaches, including MaOTLBO, NSGA‐III, MaOPSO, and MOEA/D‐DRW, in terms of GD, IGD, SP, SD, HV, and RT metrics. This can be seen from the MaF1‐MaF15 test problems, especially with four, seven, and nine objectives, and five real‐world problems that include RWMaOP1 to RWMaOP5. The findings indicate that MaOAOA outperforms the other algorithms in most of the test cases analyzed in this study.
Author Khishe, Mohammad
Jangir, Pradeep
Trivedi, Bhargavi Indrajit
Arpita
Pandya, Sundaram B.
G., Gulothungan
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CitedBy_id crossref_primary_10_1002_eng2_70268
Cites_doi 10.1109/ACCESS.2023.3270806
10.1016/j.swevo.2022.101145
10.1002/mmce.21064
10.3390/math11020413
10.1109/TEVC.2016.2519378
10.1109/TEVC.2015.2455812
10.1016/j.swevo.2020.100695
10.1007/3-540-45356-3_83
10.1109/4235.797969
10.1109/TEVC.2016.2549267
10.1016/j.cma.2006.07.010
10.1016/j.apenergy.2023.121153
10.1109/TSMC.2023.3298690
10.1109/TEVC.2018.2791283
10.1080/0305215X.2021.2021196
10.1088/2053‐1591/aa5f6a
10.1109/ACCESS.2020.2991752
10.1016/j.swevo.2021.100840
10.1016/j.camwa.2012.01.063
10.1109/TEVC.2016.2622301
10.1016/j.swevo.2018.05.004
10.1109/TEVC.2021.3118593
10.1109/TNNLS.2014.2314698
10.1007/s10489‐023‐04969‐8
10.1016/j.ejor.2006.08.008
10.1016/j.artint.2015.06.007
10.1007/s12293‐022‐00368‐7
10.1109/TEVC.2013.2262178
10.1109/TEVC.2016.2631279
10.1109/CEC.2014.6900491
10.5267/j.ijiec.2023.1.003
10.1016/j.ins.2016.09.026
10.1109/TEVC.2012.2227145
10.1109/TCYB.2020.3015998
10.1016/j.ejor.2014.05.019
10.1016/j.swevo.2020.100817
10.1007/s00500‐023‐09050‐7
10.1007/978‐3‐540‐30217‐9_84
10.1109/TEVC.2016.2600642
10.1016/j.cma.2020.113609
10.1016/j.asoc.2020.106078
10.1016/j.ins.2011.08.027
10.1016/j.engappai.2023.106454
10.1016/j.eswa.2023.122369
10.1109/TSMC.2019.2898456
10.3934/jimo.2020009
10.1109/ACCESS.2020.3036438
10.1016/j.swevo.2021.100925
10.1145/3377930.3390166
10.1007/s10489‐022‐03545‐w
10.1016/j.ins.2020.05.097
10.1007/978-3-030-72062-9_1
10.1109/TEVC.2007.892759
10.1016/j.mex.2023.102181
10.1109/TEVC.2013.2281533
10.1016/j.ins.2024.121608
10.1016/j.swevo.2020.100776
10.1016/j.jksuci.2023.101693
10.1007/s40747‐022‐00747‐0
10.1016/j.ins.2022.05.119
10.1109/TEVC.2015.2420112
10.1109/TEVC.2013.2281535
10.1109/CEC.2009.4982949
10.1080/00207543.2018.1504251
10.1038/s41598-024-76877-x
10.1016/j.swevo.2020.100794
10.1007/s40747‐017‐0039‐7
10.1109/CEC.2007.4424990
10.1162/EVCO_a_00009
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References 2021; 27
2021; 65
2023; 35
2017; 3
2017; 4
2007; 181
2020; 61
2019; 57
2023; 342
2020; 56
2004; 3242
2015; 228
2011; 19
2020; 8
2013; 17
2000
2020; 52
2024; 239
2023; 27
2022; 607
2022; 75
2020; 89
2014; 18
2022; abs/2201.05435
2012; 64
2020; 537
2023; 53
2023; 10
2023; 14
2025; 691
2023; 55
2023; 11
2017; 27
2023; 123
2015; 243
2012; 182
2009
2007
1999; 3
2008; 11
2018; 23
2024; 14
2018; 22
2021; 51
2015; 26
2021; 99
2023
2021; 376
2021
2019; 44
2020
2021; 17
2022; 8
2007; 196
2016; 20
2022; 14
2016; 374
2014
2021; 60
2021; 62
2016; 22
e_1_2_11_32_1
e_1_2_11_55_1
Perez‐Cham O. E. (e_1_2_11_7_1) 2020; 61
e_1_2_11_57_1
e_1_2_11_36_1
e_1_2_11_51_1
e_1_2_11_13_1
e_1_2_11_53_1
e_1_2_11_11_1
e_1_2_11_29_1
e_1_2_11_6_1
Palakonda V. (e_1_2_11_60_1) 2022; 607
e_1_2_11_27_1
e_1_2_11_4_1
e_1_2_11_48_1
e_1_2_11_2_1
Wei L. S. (e_1_2_11_71_1) 2023; 35
Ma L. (e_1_2_11_58_1) 2021; 99
e_1_2_11_20_1
e_1_2_11_45_1
e_1_2_11_66_1
e_1_2_11_47_1
e_1_2_11_68_1
Li J. (e_1_2_11_44_1) 2020; 537
e_1_2_11_41_1
e_1_2_11_62_1
e_1_2_11_22_1
e_1_2_11_43_1
e_1_2_11_64_1
Palakonda V. (e_1_2_11_72_1) 2024; 14
e_1_2_11_15_1
e_1_2_11_38_1
e_1_2_11_19_1
Li B. (e_1_2_11_49_1) 2014
Kukkonen S. (e_1_2_11_17_1) 2007
e_1_2_11_50_1
e_1_2_11_10_1
e_1_2_11_31_1
e_1_2_11_56_1
Shen J. (e_1_2_11_59_1) 2020; 52
e_1_2_11_14_1
e_1_2_11_35_1
e_1_2_11_52_1
Wang Q. (e_1_2_11_73_1) 2025; 691
e_1_2_11_12_1
e_1_2_11_33_1
e_1_2_11_54_1
e_1_2_11_28_1
e_1_2_11_5_1
Zhang Q. (e_1_2_11_34_1) 2008; 11
e_1_2_11_26_1
e_1_2_11_3_1
Wang Z. (e_1_2_11_24_1) 2022; 2201
e_1_2_11_21_1
e_1_2_11_67_1
e_1_2_11_46_1
e_1_2_11_25_1
e_1_2_11_40_1
e_1_2_11_63_1
e_1_2_11_9_1
e_1_2_11_23_1
e_1_2_11_42_1
e_1_2_11_65_1
e_1_2_11_18_1
Coello Coello C. A. (e_1_2_11_69_1) 2007
e_1_2_11_16_1
e_1_2_11_37_1
Palakonda V. (e_1_2_11_61_1) 2020; 8
e_1_2_11_39_1
Palakonda V. (e_1_2_11_70_1) 2023
Jiang E. (e_1_2_11_8_1) 2019; 57
Li K. (e_1_2_11_30_1) 2012; 182
References_xml – volume: 11
  issue: 2
  year: 2023
  article-title: A Many‐Objective Evolutionary Algorithm Based on indicator and Decomposition
  publication-title: Mathematics
– year: 2009
– volume: 14
  start-page: 237
  issue: 2
  year: 2022
  end-page: 251
  article-title: Adaptive Multiobjective Evolutionary Algorithm for Large‐Scale Transformer Ratio Error Estimation
  publication-title: Memetic Computing
– volume: 51
  start-page: 1
  issue: 3
  year: 2021
  end-page: 16
  article-title: An Adaptive Resource Allocation Strategy for Objective Space Partition‐Based Multiobjective Optimization
  publication-title: IEEE Transactions on Systems, Man, and Cybernetics: Systems
– volume: 243
  start-page: 423
  issue: 2
  year: 2015
  end-page: 441
  article-title: Preference‐Inspired Co‐Evolutionary Algorithms Using Weight Vectors
  publication-title: European Journal of Operational Research
– volume: 342
  year: 2023
  article-title: A Non‐Simulation‐Based Linear Model for Analytical Reliability Evaluation of Radial Distribution Systems Considering Renewable DGs
  publication-title: Applied Energy
– volume: 691
  year: 2025
  article-title: A Many‐Objective Evolutionary Algorithm Based on indicator Selection and Adaptive Angle Estimation
  publication-title: Information Sciences
– volume: 57
  start-page: 1756
  issue: 6
  year: 2019
  end-page: 1771
  article-title: An Improved Multi‐Objective Evolutionary Algorithm Based on Decomposition for Energy‐Efficient Permutation Flow Shop Scheduling Problem With Sequence‐Dependent Setup Time
  publication-title: International Journal of Production Research
– volume: 61
  year: 2020
  article-title: Automata Design for Honeybee Search Algorithm and Its Applications to 3d Scene Reconstruction and Video Tracking
  publication-title: Swarm and Evolutionary Computation
– volume: 607
  start-page: 126
  year: 2022
  end-page: 152
  article-title: An Adaptive Neighborhood‐Based Evolutionary Algorithm With Pivot‐Solution Based Selection for Multi‐and Many‐Objective Optimization
  publication-title: Information Sciences
– year: 2021
– volume: 22
  start-page: 129
  issue: 1
  year: 2018
  end-page: 142
  article-title: A Surrogate‐Assisted Reference Vector Guided Evolutionary Algorithm for Computationally Expensive Many‐Objective Optimization
  publication-title: IEEE Transactions on Evolutionary Computation
– volume: 60
  year: 2021
  article-title: Multiobjective Evolutionary Algorithm Assisted Stacked Autoencoder for Polsar Image Classification
  publication-title: Swarm and Evolutionary Computation
– volume: 3242
  start-page: 832
  year: 2004
  end-page: 842
  article-title: Indicator‐Based Selection in Multiobjective Search
  publication-title: Lecture Notes in Computer Science
– volume: 99
  start-page: 1
  year: 2021
  end-page: 13
  article-title: An Adaptive Localized Decision Variable Analysis Approach to Large‐Scale Multiobjective and Many‐Objective Optimization
  publication-title: IEEE Transactions on Cybernetics
– volume: 8
  start-page: 82781
  year: 2020
  end-page: 82796
  article-title: An Evolutionary Algorithm for Multi and Many‐Objective Optimization With Adaptive Mating and Environmental Selection
  publication-title: IEEE Access
– volume: 182
  start-page: 220
  issue: 1
  year: 2012
  end-page: 242
  article-title: Achieving Balance Between Proximity and Diversity in Multi‐Objective Evolutionary Algorithm
  publication-title: Information Sciences
– volume: 228
  start-page: 45
  year: 2015
  end-page: 65
  article-title: Bi‐Goal Evolution for Many‐Objective Optimization Problems
  publication-title: Artificial Intelligence
– volume: 4
  issue: 3
  year: 2017
  article-title: Multi‐Objective Optimization in the Development of Oil and Water Repellent Cellulose Fabric Based on Response Surface Methodology and the Desirability Function
  publication-title: Materials Research Express
– volume: 27
  issue: 2
  year: 2017
  article-title: Performance Enhancement of Multiband Antennas Through a Two‐Stage Optimization Technique
  publication-title: International Journal of RF and Microwave Computer‐Aided Engineering
– volume: 14
  start-page: 29006
  issue: 1
  year: 2024
  article-title: External Archive Guided Radial‐Grid Multi Objective Differential Evolution
  publication-title: Scientific Reports
– volume: 20
  start-page: 275
  issue: 2
  year: 2016
  end-page: 298
  article-title: A Multiobjective Evolutionary Algorithm Based on Decision Variable Analyses for Multiobjective Optimization Problems With Large‐Scale Variables
  publication-title: IEEE Transactions on Evolutionary Computation
– volume: 3
  start-page: 67
  issue: 1
  year: 2017
  end-page: 81
  article-title: A Benchmark Test Suite for Evolutionary Many‐Objective Optimization
  publication-title: Complex & Intelligent Systems
– volume: 8
  start-page: 203369
  year: 2020
  end-page: 203381
  article-title: Contribution Based Co‐Evolutionary Algorithm for Large‐Scale Optimization Problems
  publication-title: IEEE Access
– volume: 11
  start-page: 42324
  year: 2023
  end-page: 42330
  article-title: A Weight Vector Adjustment Method for Decomposition‐Based Multi‐Objective Evolutionary Algorithms
  publication-title: IEEE Access
– volume: 52
  start-page: 3645
  issue: 5
  year: 2020
  end-page: 3657
  article-title: A Controlled Strengthened Dominance Relation for Evolutionary Many‐Objective Optimization
  publication-title: IEEE Transactions on Cybernetics
– volume: 65
  year: 2021
  article-title: An Interactive Filter‐Wrapper Multi‐Objective Evolutionary Algorithm for Feature Selection
  publication-title: Swarm and Evolutionary Computation
– volume: 53
  start-page: 26949
  issue: 22
  year: 2023
  end-page: 26966
  article-title: Adaptive Fractional‐Order Genetic‐Particle Swarm Optimization Otsu Algorithm for Image Segmentation
  publication-title: Applied Intelligence
– volume: 17
  start-page: 721
  issue: 5
  year: 2013
  end-page: 736
  article-title: A Grid‐Based Evolutionary Algorithm for Many‐Objective Optimization
  publication-title: IEEE Transactions on Evolutionary Computation
– volume: 62
  year: 2021
  article-title: A Cooperative Coevolution Framework for Evolutionary Learning and Instance Selection
  publication-title: Swarm and Evolutionary Computation
– volume: 11
  start-page: 712
  issue: 6
  year: 2008
  end-page: 731
  article-title: MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition
  publication-title: IEEE Transactions on Evolutionary Computation
– volume: 64
  start-page: 944
  issue: 5
  year: 2012
  end-page: 955
  article-title: An Improved Strength Pareto Evolutionary Algorithm 2 With Application to the Optimization of Distributed Generations
  publication-title: Computers and Mathematics With Applications
– volume: 20
  start-page: 16
  issue: 1
  year: 2016
  end-page: 37
  article-title: A New Dominance Relation‐Based Evolutionary Algorithm for Many‐Objective Optimization
  publication-title: IEEE Transactions on Evolutionary Computation
– volume: 20
  start-page: 924
  issue: 6
  year: 2016
  end-page: 938
  article-title: Stochastic Ranking Algorithm for Many‐Objective Optimization Based on Multiple Indicators
  publication-title: IEEE Transactions on Evolutionary Computation
– volume: 196
  start-page: 879
  issue: 4–6
  year: 2007
  end-page: 893
  article-title: Response Surface Approximation of Pareto Optimal Front in Multi‐Objective Optimization
  publication-title: Computer Methods in Applied Mechanics and Engineering
– volume: 239
  year: 2024
  article-title: A Novel Approach to Three‐Way Decision Model Under Fuzzy Soft Dominance Degree Relations and Emergency Situation
  publication-title: Expert Systems With Applications
– volume: 537
  start-page: 203
  year: 2020
  end-page: 226
  article-title: Dmaoea‐c: Decomposition‐Based Many‐Objective Evolutionary Algorithm With the ‐Constraint Framework
  publication-title: Information Sciences
– volume: 75
  year: 2022
  article-title: ACDB‐EA: Adaptive Convergence‐Diversity Balanced Evolutionary Algorithm for Many‐Objective Optimization
  publication-title: Swarm and Evolutionary Computation
– volume: 17
  start-page: 1001
  issue: 2
  year: 2021
  end-page: 1023
  article-title: Non‐Dominated Sorting Methods for Multi‐Objective Optimization: Review and Numerical Comparison
  publication-title: Journal of Industrial and Management Optimization
– volume: 8
  start-page: 5157
  issue: 6
  year: 2022
  end-page: 5176
  article-title: A Decomposition‐Based Many‐Objective Evolutionary Algorithm With Optional Performance Indicators
  publication-title: Complex & Intelligent Systems
– volume: 55
  start-page: 650
  issue: 4
  year: 2023
  end-page: 667
  article-title: An Approach for Solving the Three‐Objective Arc Welding Robot Path Planning Problem
  publication-title: Engineering Optimization
– volume: 22
  start-page: 32
  issue: 1
  year: 2016
  end-page: 46
  article-title: Particle Swarm Optimization With a Balanceable Fitness Estimation for Many‐Objective Optimization Problems
  publication-title: IEEE Transactions on Evolutionary Computation
– volume: 14
  start-page: 293
  issue: 2
  year: 2023
  end-page: 308
  article-title: MaOTLBO: Many‐Objective Teaching‐Learning‐Based Optimizer for Control and Monitoring the Optimal Power Flow of Modern Power Systems
  publication-title: International Journal of Industrial Engineering Computations
– volume: 20
  start-page: 773
  issue: 5
  year: 2016
  end-page: 791
  article-title: A Reference Vector Guided Evolutionary Algorithm for Many‐Objective Optimization
  publication-title: IEEE Transactions on Evolutionary Computation
– volume: 23
  start-page: 173
  issue: 2
  year: 2018
  end-page: 187
  article-title: Igd Indicator‐Based Evolutionary Algorithm for Many‐Objective Optimization Problems
  publication-title: IEEE Transactions on Evolutionary Computation
– volume: 18
  start-page: 577
  issue: 4
  year: 2014
  end-page: 601
  article-title: An Evolutionary Many‐Objective Optimization Algorithm Using Reference‐Point‐Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints
  publication-title: IEEE Transactions on Evolutionary Computation
– start-page: 3983
  year: 2007
  end-page: 3990
– volume: 19
  start-page: 45
  issue: 1
  year: 2011
  end-page: 76
  article-title: Hype: An Algorithm for Fast Hypervolume‐Based Many‐Objective Optimization
  publication-title: Evolutionary Computation
– year: 2007
– volume: 181
  start-page: 1653
  issue: 3
  year: 2007
  end-page: 1669
  article-title: Sms‐Emoa: Multiobjective Selection Based on Dominated Hypervolume
  publication-title: European Journal of Operational Research
– year: 2000
– volume: 123
  year: 2023
  article-title: Problem‐Specific Knowledge MOEA/D for Energy‐Efficient Scheduling of Distributed Permutation Flow Shop in Heterogeneous Factories
  publication-title: Engineering Applications of Artificial Intelligence
– volume: 35
  issue: 8
  year: 2023
  article-title: A Many‐Objective Evolutionary Algorithm With Local Shifted Density Estimation Based on Dynamic Decomposition
  publication-title: Journal of King Saud University, Computer and Information Sciences
– volume: 376
  year: 2021
  article-title: The Arithmetic Optimization Algorithm
  publication-title: Computer Methods in Applied Mechanics and Engineering
– start-page: 2869
  year: 2014
  end-page: 2876
– volume: 18
  start-page: 450
  issue: 3
  year: 2014
  end-page: 455
  article-title: Decomposition of a Multiobjective Optimization Problem Into a Number of Simple Multiobjective Subproblems
  publication-title: IEEE Transactions on Evolutionary Computation
– start-page: 7618
  year: 2023
  end-page: 7630
  article-title: Pre‐DEMO: Preference‐Inspired Differential Evolution for Multi/Many‐Objective Optimization
  publication-title: IEEE Transactions on Systems, Man, and Cybernetics: Systems
– volume: 26
  start-page: 277
  issue: 2
  year: 2015
  end-page: 289
  article-title: Scatter Balance: An Angle‐Based Supervised Dimensionality Reduction
  publication-title: IEEE Transactions on Neural Networks and Learning Systems
– volume: abs/2201.05435
  start-page: 1
  year: 2022
  end-page: 15
  article-title: An Efficient Multi‐Indicator and Many‐Objective Optimization Algorithm Based on Two‐Archive
  publication-title: arXiv Preprint arXiv:2201.05435
– volume: 27
  start-page: 445
  issue: 3
  year: 2021
  end-page: 459
  article-title: A Fuzzy Decision Variables Framework for Large‐Scale Multiobjective Optimization
  publication-title: IEEE Transactions on Evolutionary Computation
– volume: 27
  start-page: 17729
  issue: 23
  year: 2023
  end-page: 17745
  article-title: Preference‐Inspired Coevolutionary Algorithm With Sparse Autoencoder for Many‐Objective Optimization
  publication-title: Soft Computing
– volume: 53
  start-page: 7423
  issue: 7
  year: 2023
  end-page: 7438
  article-title: A Reference Vector Adaptive Strategy for Balancing Diversity and Convergence in Many‐Objective Evolutionary Algorithms
  publication-title: Applied Intelligence
– year: 2020
– volume: 374
  start-page: 115
  year: 2016
  end-page: 134
  article-title: Many Objective Particle Swarm Optimization
  publication-title: Information Sciences
– volume: 22
  start-page: 97
  issue: 1
  year: 2018
  end-page: 112
  article-title: A Decision Variable Clustering‐Based Evolutionary Algorithm for Large‐Scale Many‐Objective Optimization
  publication-title: IEEE Transactions on Evolutionary Computation
– volume: 60
  year: 2021
  article-title: Evolutionary Many‐Objective Algorithm Based on Fractional Dominance Relation and Improved Objective Space Decomposition Strategy
  publication-title: Swarm and Evolutionary Computation
– volume: 18
  start-page: 348
  issue: 3
  year: 2014
  end-page: 365
  article-title: Shift‐Based Density Estimation for Pareto‐Based Algorithms in Many‐Objective Optimization
  publication-title: IEEE Transactions on Evolutionary Computation
– volume: 3
  start-page: 257
  issue: 4
  year: 1999
  end-page: 271
  article-title: Multiobjective Evolutionary Algorithms: A Comparative Case Study and the Strength Pareto Approach
  publication-title: IEEE Transactions on Evolutionary Computation
– volume: 56
  issue: 1
  year: 2020
  article-title: A Grey Prediction‐Based Evolutionary Algorithm for Dynamic Multiobjective Optimization
  publication-title: Swarm and Evolutionary Computation
– volume: 89
  year: 2020
  article-title: An Easy‐To‐Use Real‐World Multi‐Objective Optimization Problem Suite
  publication-title: Applied Soft Computing
– volume: 10
  year: 2023
  article-title: Many Objective Meta‐Heuristic Methods for Solving Constrained Truss Optimisation Problems: A Comparative Analysis
  publication-title: MethodsX
– volume: 44
  start-page: 404
  year: 2019
  end-page: 419
  article-title: Libea: A Lebesgue Indicator‐Based Evolutionary Algorithm for Multi‐Objective Optimization
  publication-title: Swarm and Evolutionary Computation
– ident: e_1_2_11_40_1
  doi: 10.1109/ACCESS.2023.3270806
– ident: e_1_2_11_23_1
  doi: 10.1016/j.swevo.2022.101145
– ident: e_1_2_11_67_1
  doi: 10.1002/mmce.21064
– ident: e_1_2_11_25_1
  doi: 10.3390/math11020413
– ident: e_1_2_11_41_1
  doi: 10.1109/TEVC.2016.2519378
– ident: e_1_2_11_56_1
  doi: 10.1109/TEVC.2015.2455812
– ident: e_1_2_11_47_1
  doi: 10.1016/j.swevo.2020.100695
– ident: e_1_2_11_10_1
  doi: 10.1007/3-540-45356-3_83
– volume: 2201
  start-page: 1
  year: 2022
  ident: e_1_2_11_24_1
  article-title: An Efficient Multi‐Indicator and Many‐Objective Optimization Algorithm Based on Two‐Archive
  publication-title: arXiv Preprint arXiv:2201.05435
– ident: e_1_2_11_31_1
  doi: 10.1109/4235.797969
– ident: e_1_2_11_18_1
  doi: 10.1109/TEVC.2016.2549267
– ident: e_1_2_11_68_1
  doi: 10.1016/j.cma.2006.07.010
– ident: e_1_2_11_3_1
  doi: 10.1016/j.apenergy.2023.121153
– start-page: 7618
  year: 2023
  ident: e_1_2_11_70_1
  article-title: Pre‐DEMO: Preference‐Inspired Differential Evolution for Multi/Many‐Objective Optimization
  publication-title: IEEE Transactions on Systems, Man, and Cybernetics: Systems
  doi: 10.1109/TSMC.2023.3298690
– ident: e_1_2_11_26_1
  doi: 10.1109/TEVC.2018.2791283
– ident: e_1_2_11_4_1
  doi: 10.1080/0305215X.2021.2021196
– ident: e_1_2_11_66_1
  doi: 10.1088/2053‐1591/aa5f6a
– volume: 8
  start-page: 82781
  year: 2020
  ident: e_1_2_11_61_1
  article-title: An Evolutionary Algorithm for Multi and Many‐Objective Optimization With Adaptive Mating and Environmental Selection
  publication-title: IEEE Access
  doi: 10.1109/ACCESS.2020.2991752
– ident: e_1_2_11_48_1
  doi: 10.1016/j.swevo.2021.100840
– volume: 99
  start-page: 1
  year: 2021
  ident: e_1_2_11_58_1
  article-title: An Adaptive Localized Decision Variable Analysis Approach to Large‐Scale Multiobjective and Many‐Objective Optimization
  publication-title: IEEE Transactions on Cybernetics
– ident: e_1_2_11_11_1
  doi: 10.1016/j.camwa.2012.01.063
– ident: e_1_2_11_43_1
  doi: 10.1109/TEVC.2016.2622301
– ident: e_1_2_11_29_1
  doi: 10.1016/j.swevo.2018.05.004
– ident: e_1_2_11_54_1
  doi: 10.1109/TEVC.2021.3118593
– ident: e_1_2_11_45_1
  doi: 10.1109/TNNLS.2014.2314698
– ident: e_1_2_11_6_1
  doi: 10.1007/s10489‐023‐04969‐8
– ident: e_1_2_11_27_1
  doi: 10.1016/j.ejor.2006.08.008
– ident: e_1_2_11_53_1
  doi: 10.1016/j.artint.2015.06.007
– ident: e_1_2_11_2_1
  doi: 10.1007/s12293‐022‐00368‐7
– ident: e_1_2_11_22_1
  doi: 10.1109/TEVC.2013.2262178
– ident: e_1_2_11_50_1
  doi: 10.1109/TEVC.2016.2631279
– start-page: 2869
  volume-title: In 2014 IEEE Congress on Evolutionary Computation (CEC)
  year: 2014
  ident: e_1_2_11_49_1
  doi: 10.1109/CEC.2014.6900491
– ident: e_1_2_11_36_1
  doi: 10.5267/j.ijiec.2023.1.003
– ident: e_1_2_11_37_1
  doi: 10.1016/j.ins.2016.09.026
– ident: e_1_2_11_15_1
  doi: 10.1109/TEVC.2012.2227145
– volume: 52
  start-page: 3645
  issue: 5
  year: 2020
  ident: e_1_2_11_59_1
  article-title: A Controlled Strengthened Dominance Relation for Evolutionary Many‐Objective Optimization
  publication-title: IEEE Transactions on Cybernetics
  doi: 10.1109/TCYB.2020.3015998
– ident: e_1_2_11_52_1
  doi: 10.1016/j.ejor.2014.05.019
– volume: 61
  year: 2020
  ident: e_1_2_11_7_1
  article-title: Automata Design for Honeybee Search Algorithm and Its Applications to 3d Scene Reconstruction and Video Tracking
  publication-title: Swarm and Evolutionary Computation
  doi: 10.1016/j.swevo.2020.100817
– ident: e_1_2_11_51_1
  doi: 10.1007/s00500‐023‐09050‐7
– ident: e_1_2_11_28_1
  doi: 10.1007/978‐3‐540‐30217‐9_84
– ident: e_1_2_11_57_1
  doi: 10.1109/TEVC.2016.2600642
– ident: e_1_2_11_62_1
  doi: 10.1016/j.cma.2020.113609
– ident: e_1_2_11_64_1
  doi: 10.1016/j.asoc.2020.106078
– volume: 182
  start-page: 220
  issue: 1
  year: 2012
  ident: e_1_2_11_30_1
  article-title: Achieving Balance Between Proximity and Diversity in Multi‐Objective Evolutionary Algorithm
  publication-title: Information Sciences
  doi: 10.1016/j.ins.2011.08.027
– ident: e_1_2_11_5_1
  doi: 10.1016/j.engappai.2023.106454
– ident: e_1_2_11_14_1
  doi: 10.1016/j.eswa.2023.122369
– ident: e_1_2_11_33_1
  doi: 10.1109/TSMC.2019.2898456
– ident: e_1_2_11_13_1
  doi: 10.3934/jimo.2020009
– ident: e_1_2_11_55_1
  doi: 10.1109/ACCESS.2020.3036438
– ident: e_1_2_11_46_1
  doi: 10.1016/j.swevo.2021.100925
– ident: e_1_2_11_20_1
  doi: 10.1145/3377930.3390166
– ident: e_1_2_11_12_1
  doi: 10.1007/s10489‐022‐03545‐w
– volume: 537
  start-page: 203
  year: 2020
  ident: e_1_2_11_44_1
  article-title: Dmaoea‐c: Decomposition‐Based Many‐Objective Evolutionary Algorithm With the ‐Constraint Framework
  publication-title: Information Sciences
  doi: 10.1016/j.ins.2020.05.097
– ident: e_1_2_11_21_1
  doi: 10.1007/978-3-030-72062-9_1
– volume: 11
  start-page: 712
  issue: 6
  year: 2008
  ident: e_1_2_11_34_1
  article-title: MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition
  publication-title: IEEE Transactions on Evolutionary Computation
  doi: 10.1109/TEVC.2007.892759
– ident: e_1_2_11_65_1
  doi: 10.1016/j.mex.2023.102181
– ident: e_1_2_11_42_1
  doi: 10.1109/TEVC.2013.2281533
– volume: 691
  year: 2025
  ident: e_1_2_11_73_1
  article-title: A Many‐Objective Evolutionary Algorithm Based on indicator Selection and Adaptive Angle Estimation
  publication-title: Information Sciences
  doi: 10.1016/j.ins.2024.121608
– ident: e_1_2_11_19_1
  doi: 10.1016/j.swevo.2020.100776
– volume: 35
  issue: 8
  year: 2023
  ident: e_1_2_11_71_1
  article-title: A Many‐Objective Evolutionary Algorithm With Local Shifted Density Estimation Based on Dynamic Decomposition
  publication-title: Journal of King Saud University, Computer and Information Sciences
  doi: 10.1016/j.jksuci.2023.101693
– ident: e_1_2_11_39_1
  doi: 10.1007/s40747‐022‐00747‐0
– volume: 607
  start-page: 126
  year: 2022
  ident: e_1_2_11_60_1
  article-title: An Adaptive Neighborhood‐Based Evolutionary Algorithm With Pivot‐Solution Based Selection for Multi‐and Many‐Objective Optimization
  publication-title: Information Sciences
  doi: 10.1016/j.ins.2022.05.119
– ident: e_1_2_11_16_1
  doi: 10.1109/TEVC.2015.2420112
– ident: e_1_2_11_38_1
  doi: 10.1109/TEVC.2013.2281535
– ident: e_1_2_11_35_1
  doi: 10.1109/CEC.2009.4982949
– volume: 57
  start-page: 1756
  issue: 6
  year: 2019
  ident: e_1_2_11_8_1
  article-title: An Improved Multi‐Objective Evolutionary Algorithm Based on Decomposition for Energy‐Efficient Permutation Flow Shop Scheduling Problem With Sequence‐Dependent Setup Time
  publication-title: International Journal of Production Research
  doi: 10.1080/00207543.2018.1504251
– volume-title: Evolutionary Algorithms for Solving Multi‐Objective Problems
  year: 2007
  ident: e_1_2_11_69_1
– volume: 14
  start-page: 29006
  issue: 1
  year: 2024
  ident: e_1_2_11_72_1
  article-title: External Archive Guided Radial‐Grid Multi Objective Differential Evolution
  publication-title: Scientific Reports
  doi: 10.1038/s41598-024-76877-x
– ident: e_1_2_11_9_1
  doi: 10.1016/j.swevo.2020.100794
– ident: e_1_2_11_63_1
  doi: 10.1007/s40747‐017‐0039‐7
– start-page: 3983
  volume-title: In 2007 IEEE Congress on Evolutionary Computation
  year: 2007
  ident: e_1_2_11_17_1
  doi: 10.1109/CEC.2007.4424990
– ident: e_1_2_11_32_1
  doi: 10.1162/EVCO_a_00009
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Snippet ABSTRACT Currently, the use of multi‐objective optimization algorithms has been applied in many fields to find the efficient solution of the multiple objective...
Currently, the use of multi‐objective optimization algorithms has been applied in many fields to find the efficient solution of the multiple objective...
ABSTRACT Currently, the use of multi‐objective optimization algorithms has been applied in many fields to find the efficient solution of the multiple objective...
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Publisher
SubjectTerms Algorithms
Arithmetic
Convergence
Decision making
Decomposition
Genetic algorithms
information feedback mechanism
many‐objective arithmetic optimization algorithm
many‐objective optimization
Mathematical analysis
metaheuristic algorithm
Multiple objective analysis
Objectives
Optimization
Pareto optimality
Pareto optimum
Variables
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Title MaOAOA: A Novel Many‐Objective Arithmetic Optimization Algorithm for Solving Engineering Problems
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