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

Abstract

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.

Hybrid artificial electric field employing cuckoo search algorithm with refraction learning for engineering optimization problems

“…Experiments are carried out on 20 standard benchmark functions to confirm the efficiency of AEFA-CSR for solving global optimization functions. The algorithms; Artificial Electric Field Algorithm (AEFA) 9 , Cuckoo Search (CS) 47 , Differential Evolution (DE) 6 , Firefly Algorithm (FA) 8 , Particle Swarm Optimization (PSO) 4 , Jaya Algorithm (JAYA) 57 , Hybrid-Flash Butterfly Optimization Algorithm (HFBOA) 58 , Sand Cat Swarm Optimization (SCSO) 59 , Salp Swarm Algorithm with Local Escaping Operator (SSALEO) 60 , Transient Search Optimization (TSO) 61 and Chaotic Hybrid Butterfly Optimization Algorithm with Particle Swarm (HPSOBOA) 62 were chosen for a detailed observation. The algorithms are chosen in a way to give a better insight to the readers such as well-studied and commonly used ones, recently developed ones which gained attention from the researchers in a short period of time and finally hybrid algorithms that are made up of powerful optimizers.…”

“…Tension/compression spring optimization design problem. The tension/compression spring design problem's optimization objective is to lower the spring weight 60 . It is a continuous constrained problem and the variables are wire diameter d, average coil diameter D, and effective coil number P. Constraints include subject to minimal deviation (g 1 ), shear stress (g 2 ), shock frequency (g 3 ), and outside diameter limit (g 4 ).…”

Heliostat Field Layout via Niching and Elite Competition Swarm Optimization

“…In the future, our research will explore large-scale heliostat field optimization in uncertain environments. Additionally, we aim to extend the application of NECSO to other domains such as blockchain problems [59], global optimization problems [60], and distributed computing problems [61].…”

Fine-Tuned Cardiovascular Risk Assessment: Locally Weighted Salp Swarm Algorithm in Global Optimization

“…The second stage, a competitive learning strategy, is employed to revise the condition of the followers by allowing them to learn from the leading member. Problems with population diversity, unequal exploration and exploitation, and premature convergence are all handled by the one-of-a-kind search strategy known as SSALEO [36]. The SSA has these issues, but SSALEO is able to solve them.…”

“…The SSA leverages these principles to guide the search process, facilitating the identification of optimal solutions across a spectrum of domains. The algorithm's unique features, including its adaptive mechanisms and its capacity to adapt to dynamic environments, render it a compelling subject of investigation and application within various scientific disciplines [10,[33][34][35][36]. Nonetheless, the standard SSA faces some limitations that hinder its search abilities.…”