Comparison Study of Discrete Optimization Problem Using Meta-Heuristic Approaches: A Case Study
Ali Ahmid, Thiên-My Dao, Van Ngan Lê · International Journal of Industrial Engineering and Operations Management · 2019
This paper presents the performance comparison of five meta-heuristic algorithms to solve a discrete optimization problem. The comparison is undertaken for a case of simply supported plate subjected to biaxial loading conditions. Furthermore, the optimization objective is to determine the optimal stacking sequence design of a laminate that maximizes the critical buckling load factor (λcb). The chosen meta-heuristics have been implemented using MATLAB with the same convergence criteria and the same maximum number of iterations to ensure a fair comparison. The implemented assessment criterion has performance measures of average CPU time, solution price, reliability, and normalized price. The results have demonstrated the outperformance of the Ant Colony Optimization Algorithm (ACOA) over other algorithms, which confirms the findings of previous studies. Moreover, the Tabu search algorithm (TS) and the Discrete Particle Swarm Optimization algorithm (DPSO) performed poorly due to their limited exploration capability. Additionally, the Genetic Algorithm (GA) and the Simulated Annealing algorithm (SA) exhibited a high level of reliability but showed an expensive solution cost. This study presents an adequate comparison approach of meta-heuristics, where it extends the comparison scope to cover the performance analysis of meta-heuristics more than that previously done in the domain of stacking sequence design optimization.