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A hybrid slime mould algorithm for global optimization
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AI
DOI：10.1007/s11042-022-14077-3.png)
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Abstract
En 中文
The local optima stagnation is a major issue with all meta-heuristic algorithms. In this paper, a hybrid slime mould algorithm (SMA) is proposed with the aid of quadratic approximation to address the aforesaid problem to expedite the explorative strength of slime mould in nature. As quadratic approximation performs better within the local confinement region, so the QA has been incorporated with SMA to propose the hybrid HSMA to improve the exploitation ability of the algorithm so that global optimum can be achieved. The effectiveness of the proposed algorithm has been compared with classical SMA, some state-of-the-art metaheuristics, some PSO variants using 20 benchmark problems and IEEE CEC 2017 suite. Convergence analysis and statistical tests are performed to validate the supremacy of the proposed algorithm. Moreover, three real-world engineering optimization problems are solved, and solutions are compared with various algorithms. Results and their analyses convey the fruitfulness of the proposed algorithm by showing encouraging performance on different search landscapes.
Keywords:
Metaheuristics
Swarm intelligence
Slime Mould algorithm
Quadratic approximation
Hybrid algorithm
Journal
IF:
3
Papers:
1.9W
Citations:
3.2W