article · IEEE Access
Complex optimisation problems in engineering frequently require robust computational search algorithms. While the Salp Swarm Algorithm is effective, it often suffers from slow convergence and stagnation in suboptimal local solutions. To address these limitations, the algorithm was combined with a local escaping operator to create an enhanced variant called SSALEO. This hybrid method improves local search efficiency, preserves population diversity, and balances exploration and exploitation to prevent premature convergence. The technique was evaluated using standard benchmark test functions featuring up to one thousand decision variables, alongside seven constrained engineering design challenges. Comparative statistical tests confirmed that the addition of the local escaping operator accelerates convergence, enhances solution accuracy, and enables the algorithm to outperform several established metaheuristics as well as specialised state-of-the-art optimisers like CMA-ES and SHADE.
Solving complex, high-dimensional engineering problems often fails when computational algorithms get trapped in suboptimal solutions or take too long to run. By preventing algorithmic stagnation and accelerating the search process, this approach provides engineers and computational researchers with a more dependable mathematical tool for resolving demanding, constrained design challenges accurately and efficiently.
The method is applicable to engineering design challenges that require constrained, large-scale numerical optimisation. Target users include computational engineers, software developers integrating optimisation toolboxes, and industrial system designers. The technology is currently at the stage of algorithm development and benchmark validation, having demonstrated efficacy on standardised engineering problem sets rather than being packaged into a dedicated commercial software product.
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The Salp Swarm Algorithm (SSA) outperforms well-known algorithms such as particle swarm optimizers and grey wolf optimizers in complex optimization challenges. However, like most meta-heuristic algorithms, SSA suffers from slow convergence and stagnation in the best local solution. In this study, a Salp swarm algorithm (SSA) is combined with a local escaping operator (LEO) to overcome some inherent limitations of the original SSA. SSALEO is a novel search technique that accounts for population diversity, the imbalance between exploitation and exploration, and the SSA algorithm’s premature convergence. By implementing LEO in SSALEO, the search slowdown in SSA is eliminated, and the local search efficiency of swarm agents is improved. The proposed SSALEO method is tested using the CEC 2017 benchmark with 50 and 100 decision variables, seven CEC2008lsgo test functions with 200, 500, and 1000 decision variables, and its performance was compared to other metaheuristic algorithms (MAs) and advanced algorithms, including seven Salp swarm variants. The comparisons show that SSA greatly benefits from LEO by enhancing the quality and accelerating its solutions’ convergence rate. The SSALEO was then assessed using a benchmark set of seven well-known constrained design challenges in various engineering domains defined in the CEC 2020 conference benchmark. Friedman and Wilcoxon rank-sum statistical tests are also used to examine the results. ACCORDING TO EXPERIMENTAL DATA AND STATISTICAL TESTS, the SSALEO algorithm is very competitive and often superior to the algorithms used in the studies. Further, the proposed approach can be viewed as a special LSGO optimizer whose performance exceeds that of specialized state-of-the-art algorithms like CMA-ES and SHADE.
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DOI: 10.1109/access.2022.3202894
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