This study introduces Multi-Objective Chaos Game Optimization (MOCGO), a metaheuristic algorithm developed for the purpose of handling multi-objective structural optimization problems with constraints that performs significantly better than other algorithms, including NSGA-II, MOGOA, MOALO, and MOAVOA.
Abstract
This study introduces Multi-Objective Chaos Game Optimization (MOCGO), a metaheuristic algorithm developed for the purpose of handling multi-objective structural optimization problems with constraints. In doing so, MOCGO utilizes a single-objective algorithm known as Chaos Game Optimization (CGO) in conjunction with chaos exploration dynamics, a diversity-preserving archive, and a grid-selection process to create an effective set of Pareto optimal solutions. This algorithm has been tested on six different trusses (10-bar to 120-bar) based on the problem of minimizing the weight and compliance of a structure while satisfying the stress constraint criteria. As measures of fitness, MOCGO performs significantly better than other algorithms, including NSGA-II, MOGOA, MOALO, and MOAVOA. These results demonstrate MOCGO as an efficient and robust approach for constrained multi-objective structural design.
The proposed Multi-Objective Narwhal Optimizer (MONO), a Pareto-based extension of the recently developed Narwhal Optimizer, incorporates Pareto dominance, external archive management, adaptive multi-leader guidance, and crowding-distance-based diversity preservation to effectively balance convergence and exploration t...
S. Medjahed, Mourad Bouatouche, Fatima Boukhatem· Journal of Supercomputing· 0 citations
This research examines the suitability of two new local search-based metaheuristics (i.e., Simulated Annealing (SA) and Particle Swarm Optimisation (PSO)) for addressing single-machine scheduling problems in terms of two complementary objective functions: and . We also analyze the interaction of the two objective...
Iraq T. Abbas, R. J. Mitlif· Iraqi Journal of Science· 0 citations
An overview of the conceptual review of Hybrid Salp Swarm–Genetic Algorithm optimization in Multidimensional Knapsack Problem outlines the development of the MKP, metaheuristic optimization, evolutionary computation, swarm intelligence and hybrid optimization and discusses the complementary nature of exploring/exploiti...
Asaju La’aro Bolaji, Sanfo Bala, Andrew Ishaku Wreford et al.· Middle East Journal of Appli...· 0 citations
Among the evaluated methods, PSO consistently provides the best overall performance by achieving faster convergence and superior solution quality under limited function evaluations, while GWO and WOA exhibit competitive and robust performance across challenging benchmark problems.
Chong-Wen Huang, Hao-Xiang Lei, Cheng-Yi Li et al.· Journal of Applied Technolog...· 0 citations
A novel evolutionary algorithm termed CMOEA-TSD is proposed, which adopts a three-population, two-stage co-evolutionary framework to enhance diversity and coordinate multiple search behaviors and significantly outperforms seven state-of-the-art algorithms.
This work presents an enhanced multi-objective dung beetle optimization algorithm that is implemented to tackle the multi-objective path planning optimization problem for mobile robot, and numerical simulation results verify that the method achieves sound performance in resolving practical engineering issues.
