Comparing SSALEO as a Scalable Large Scale Global Optimization Algorithm to High-Performance Algorithms for Real-World Constrained Optimization Benchmark

The Salp Swarm Algorithm (SSA) outperforms well-known algorithms such as particle swarm optimizers and grey wolf in complex optimization challenges. However, like most meta-heuristic algorithms, SSA suffers from slow convergence stagnation the best local solution. In this study, a algorithm is combined with escaping operator (LEO) to overcome some inherent limitations of original SSA. SSALEO novel search technique that accounts for population diversity, imbalance between exploitation exploration, algorithm’s premature convergence. By implementing LEO SSALEO, slowdown eliminated, efficiency agents improved. proposed method tested using CEC 2017 benchmark 50 100 decision variables, seven CEC2008lsgo test functions 200, 500, 1000 its performance was compared other metaheuristic (MAs) advanced including variants. comparisons show greatly benefits by enhancing quality accelerating solutions’ rate. then assessed set constrained design challenges various engineering domains defined 2020 conference benchmark. Friedman Wilcoxon rank-sum statistical tests are also used examine results. ACCORDING TO EXPERIMENTAL DATA AND STATISTICAL TESTS, very competitive often superior studies. Further, approach can be viewed special LSGO optimizer whose exceeds specialized state-of-the-art CMA-ES SHADE.