Journal
COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING
Volume 25, Issue 3, Pages 149-170Publisher
WILEY
DOI: 10.1111/j.1467-8667.2009.00610.x
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Funding
- Aeronautical Research and Development Board, New Delhi [DARO/08/1051334/M/I]
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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).
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