Suchergebnisse - branch AND bound knapsack (problem OR problems) multiobjective evolutionary algorithms

Solving multiobjective, multiconstraint knapsack problems using mathematical programming and evolutionary algorithms

Autoren: Florios, Kostas Mavrotas, George Diakoulaki, Danae

Quelle: European Journal of Operational Research. May2010, Vol. 203 Issue 1, p14-21. 8p.

Schlagwörter: *ALGORITHMS, *MATHEMATICAL programming, *HEURISTIC, *OPERATIONS research, KNAPSACK problems, EVOLUTIONARY computation, BRANCH & bound algorithms, METRIC system

Multiobjective Evolutionary Algorithms: A Comparative Case Study and the Strength Pareto Approach.

Autoren: Zitzler, Eckart Thiele, Lothar

Quelle: IEEE Transactions on Evolutionary Computation; Nov1999, Vol. 3 Issue 4, p257, 15p

Schlagwörter: ALGORITHMS, KNAPSACK problems, CLUSTER analysis (Statistics), SYSTEMS software, MATHEMATICAL optimization

Improvement of genetic algorithms with decomposition procedures for large-scale multiobjective multidimensional 0-1 knapsack problems incorporating fuzzy goals.

Autoren: Kato, Kosuke Sakawa, Masatoshi Ikegame, Toshinori

Quelle: Electronics & Communications in Japan, Part 3: Fundamental Electronic Science; Dec2000, Vol. 83 Issue 12, p62-69, 8p

Schlagwörter: KNAPSACK problems, INTEGER programming, BRANCH & bound algorithms, MATHEMATICAL programming, GENETIC algorithms

Advancing local search approximations for multiobjective combinatorial optimization problems.

Autoren: Weerasena, Lakmali

Quelle: Journal of Combinatorial Optimization; Apr2022, Vol. 43 Issue 3, p589-612, 24p

Computationally efficient evolutionary optimisation for joint reservoir design-operations.

Autoren: Motevali Bashi Naeini, Elaheh Soltaninia, Shahrokh

Quelle: Proceedings of ICE: Water Management; Jun2023, Vol. 176 Issue 3, p132-147, 16p

Comparison between Single and Multi-Objective Evolutionary Algorithms to Solve the Knapsack Problem and the Travelling Salesman Problem.

Autoren: Mahrach, Mohammed Miranda, Gara León, Coromoto et al.

Quelle: Mathematics (2227-7390); Nov2020, Vol. 8 Issue 11, p2018-2018, 1p

Schlagwörter: TRAVELING salesman problem, KNAPSACK problems, PROBLEM solving, EVOLUTIONARY algorithms, EVOLUTIONARY computation

Improving the performance of evolutionary algorithms for the multiobjective 0/1 knapsack problem using ɛ -dominance.

Autoren: Grosan, C.

Quelle: Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753); 2004, p1958-1958, 1p

Compressed representation for higher-level meme space evolution: a case study on big knapsack problems.

Autoren: Feng, Liang Gupta, Abhishek Ong, Yew-Soon

Quelle: Memetic Computing; Mar2019, Vol. 11 Issue 1, p3-17, 15p

An exploratory research of elitist probability schema and its applications in evolutionary algorithms.

Autoren: Zhang, Hong-Guang Liu, Yuan-An Tang, Bi-Hua et al.

Quelle: Applied Intelligence; Jun2014, Vol. 40 Issue 4, p695-709, 15p

Schlagwörter: COMBINATORIAL optimization, GENETIC algorithms, GENETIC programming, PARTICLE swarm optimization, SWARM intelligence

Multiobjective Optimization and Evolutionary Algorithms for the Application Mapping Problem in Multiprocessor System-on-Chip Design.

Autoren: Erbas, Cagkan Cerav-Erbas, Selin Pimentel, Andy D.

Quelle: IEEE Transactions on Evolutionary Computation; Jun2006, Vol. 10 Issue 3, p358-374, 17p, 3 Diagrams, 4 Charts, 11 Graphs

Schlagwörter: COMPUTER software, EMBEDDED computer systems, INTEGER programming, CASE studies, MATHEMATICAL mappings, INHOMOGENEOUS materials

Players’ selection for basketball teams, through Performance Index Rating, using multiobjective evolutionary algorithms.

Autoren: Pérez-Toledano, Miguel Ángel Rodriguez, Francisco J. García-Rubio, Javier et al.

Quelle: PLoS ONE; 9/4/2019, Vol. 14 Issue 9, p1-20, 20p

Schlagwörter: EVOLUTIONARY algorithms, BASKETBALL teams, SPORTS competitions, SPORTS administration, BIOLOGICAL evolution, DIFFERENTIAL evolution

Geografische Kategorien: SPAIN

Firma/Körperschaft: EUROPEAN Union

Solving multiobjective, multiconstraint knapsack problems using mathematical programming and evolutionary algorithms

Autoren: Florios, Kostas Mavrotas, George Diakoulaki, Danae

Relation: http://www.sciencedirect.com/science/article/pii/S0377-2217(09)00497-4

Verfügbarkeit: http://www.sciencedirect.com/science/article/pii/S0377-2217(09)00497-4

Considerations in Engineering Parallel Multiobjective Evolutionary Algorithms.

Autoren: Van Veldhuizen, David A. Zydallis, Jesse B. Lamont, Gary B.

Quelle: IEEE Transactions on Evolutionary Computation; Apr2003, Vol. 7 Issue 2, p144, 30p

Schlagwörter: ALGORITHMS, RESEARCH, SYMMETRIC domains, MATHEMATICAL notation

QUANTUMIZED GENETIC ALGORITHM FOR SEGMENTATION AND OPTIMIZATION TASKS.

Autoren: Sabeti, Malihe Karimi, Laleh Honarvar, Naemeh et al.

Quelle: Biomedical Engineering: Applications, Basis & Communications; Jun2020, Vol. 32 Issue 03, pN.PAG-N.PAG, 11p

Schlagwörter: IMAGE segmentation, GENETIC algorithms, PARTICLE swarm optimization, MATHEMATICAL optimization, THRESHOLDING algorithms, COMPUTER vision

Concrete hyperheuristic framework for test case prioritization.

Autoren: Bian, Yi Li, Zheng Guo, Junxia et al.

Quelle: Journal of Software: Evolution & Process; Nov2018, Vol. 30 Issue 11, pN.PAG-N.PAG, 1p

Schlagwörter: REGRESSION testing (Computer science), ITERATIVE methods (Mathematics), EVOLUTIONARY algorithms, LEARNING strategies, HEURISTIC

Parallel implementation of an exact two-phase method for the biobjective knapsack problem.

Autoren: Chaabane, Khadidja Bouroubi, Sadek Djellouli, Younes

Quelle: RAIRO: Operations Research (2804-7303); 2024, Vol. 58 Issue 4, p3347-3367, 21p

Schlagwörter: COMBINATORIAL optimization, PARALLEL programming, TEST methods, ALGORITHMS, KNAPSACK problems, LITERATURE

Reinforcement Learning for Solving the Knapsack Problem.

Autoren: Zhang, Zhenfu Yin, Haiyan Zuo, Liudong et al.

Quelle: Computers, Materials & Continua; 2025, Vol. 84 Issue 1, p919-936, 18p

Schlagwörter: REINFORCEMENT learning, TIME complexity, DYNAMIC programming, COMBINATORIAL optimization, PROBLEM solving, KNAPSACK problems

Scheduling multi-configuration last-mile delivery logistics by learning from optimisation feedback and customer preferences.

Autoren: Tafakkori, Keivan Tavakkoli-Moghaddam, Reza Siadat, Ali

Quelle: International Journal of Production Research; Nov2025, Vol. 63 Issue 21, p7835-7864, 30p

Schlagwörter: LOGISTICS, SCHEDULING, DELIVERY of goods, PREDICTION models, OPERATIONS research, CONSUMER preferences, EVOLUTIONARY algorithms

A Heuristics-Guided Simplified Discrete Harmony Search Algorithm for Solving 0-1 Knapsack Problem.

Autoren: Zheng, Fuyuan Cheng, Kanglong Yang, Kai et al.

Quelle: Algorithms; May2025, Vol. 18 Issue 5, p295, 31p

Schlagwörter: SEARCH algorithms, RANDOM operators, KNAPSACK problems, ALGORITHMS, BACKPACKS, HEURISTIC

Nature-Inspired Algorithms and Applications

Autoren: S. Balamurugan Anupriya Jain Sachin Sharma et al.

Resource Type: eBook.

Schlagworte: Nature-inspired algorithms

Categories: COMPUTERS / Computer Engineering