Hybrid slime mould algorithm with adaptive guided differential evolution algorithm for combinatorial and global optimization problems

•SMA-AGDE method is proposed for solving various optimization problems.•The algorithm performance is verified on CEC’17 benchmark.•The method performance is verified on 3 engineering and 2 combinatorial problems.•Efficiency of the proposed method is compared with many metaheuristics. The Slime Mould...

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Bibliographic Details
Published in:Expert systems with applications Vol. 174; p. 114689
Main Authors: Houssein, Essam H., Mahdy, Mohamed A., Blondin, Maude J., Shebl, Doaa, Mohamed, Waleed M.
Format: Journal Article
Language:English
Published: New York Elsevier Ltd 15.07.2021
Elsevier BV
Subjects:
Adaptive algorithms
Adaptive guided differential evolution algorithm (AGDE)
Algorithms
Combinatorial analysis
Combinatorial optimization problems
Compression springs
Compression tests
Covariance matrix
Design engineering
Design optimization
Engineering design problems
Evolutionary algorithms
Evolutionary computation
Exploration and exploitation
Global optimization
Global optimization problems
Heuristic methods
Machine learning
Metaheuristics
Mutation
Optimization
Performance evaluation
Pressure vessels
Roller bearings
Search algorithms
Slime
Slime mould algorithm (SMA)
Exploration and exploitation
Combinatorial optimization problems
Global optimization problems
Engineering design problems
Metaheuristics
Slime mould algorithm (SMA)
Adaptive guided differential evolution algorithm (AGDE)
ISSN:0957-4174, 1873-6793
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Summary:•SMA-AGDE method is proposed for solving various optimization problems.•The algorithm performance is verified on CEC’17 benchmark.•The method performance is verified on 3 engineering and 2 combinatorial problems.•Efficiency of the proposed method is compared with many metaheuristics. The Slime Mould Algorithm (SMA) is a recent metaheuristic inspired by the oscillation of slime mould. Similar to other original metaheuristic algorithms (MAs), SMA may suffer from drawbacks, such as being trapped in minimum local regions and improper balance between exploitation and exploration phases. To overcome these weaknesses, this paper proposes a hybrid algorithm: SMA combined to Adaptive Guided Differential Evolution Algorithm (AGDE) (SMA-AGDE). The AGDE mutation method is employed to enhance the swarm agents’ local search, increase the population’s diversity, and help avoid premature convergence. The SMA-AGDE’s performance is evaluated on the CEC’17 test suite, three engineering design problems – tension/compression spring, pressure vessel, and rolling element bearing – and two combinatorial optimization problems – bin packing and quadratic assignment. The SMA-AGDE is compared with three categories of optimization methods: (1) The well-studied MAs, i.e., Biogeography-Based Optimizer (BBO), Gravitational Search Algorithm (GSA), and Teaching Learning-Based Optimization (TLBO), (2) Recently developed MAs, i.e., Harris Hawks Optimization (HHO), Manta Ray Foraging optimization (MRFO), and the original SMA, and (3) High-performance MAs, i.e., Evolution Strategy with Covariance Matrix Adaptation (CMA-ES), and AGDE. The overall simulation results reveal that the SMA-AGDE ranked first among the compared algorithms, and so, over different function landscapes. Thus, the proposed SMA-AGDE is a promising optimization tool for global and combinatorial optimization problems and engineering design problems.
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ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2021.114689