In this paper we propose a new meta-heuristic algorithm for solving combinatorial optimization problems. The proposed algorithm follows the scatter search structure developed earlier for single objective combinatorial optimisation, but uses crossover operator borrowed from the field of evolutionary algorithms. The resulting hybrid algorithm is built with typical features like Pareto dominance, density estimation, and an external archive to store the non-dominated solutions in order to handle multiple objectives. The performance of the proposed multi-objective scatter search algorithm is demonstrated by solving a laminate composite cylindrical shell subjected to both combinatorial as well as design constraints. Further, the proposed algorithm is compared with four state-of-the-art multi-objective optimizers: Non-dominated sorting Genetic Algorithm (NSGA-II), Pareto Archived Evolutionary Strategy (PAES) and Micro GA. The studies presented in this paper indicate that proposed algorithm produces very competitive Pareto fronts according to the applied convergence metric and it clearly outperforms the other three algorithms
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