A Meta‐Heuristic Algorithm for Multi‐Objective Optimal Design of Hybrid Laminate Composite Structures

Abstract

Abstract: In this article, we propose a meta‐heuristic algorithm for solving multi‐objective combinatorial optimization problems. The proposed multi‐objective combinatorial optimization algorithm is developed by combining the good features of popular guided local search algorithms like simulated annealing (SA) and tabu search (TS). It has been organized as a multiple start algorithm to maintain a good balance between intensification and diversification. The proposed meta‐heuristic algorithm is evaluated by solving the stacking sequence optimization of hybrid fiber‐reinforced composite plate, cylindrical shell, and pressure vessel problems. The standard performance metrics for evaluating multi‐objective optimization algorithms are used to demonstrate the effectiveness of the proposed algorithm over other popular evolutionary algorithms like Nondominated Sorting Genetic Algorithms (NSGA‐II), Pareto Archived Evolutionary Strategy (PAES), micro‐GA, and Multi‐Objective Particle Swarm Optimization (MOPSO).