A memetic algorithm for combinatorial problems with multiple objectives

TL;DR: The studies presented in this paper indicate that proposed memetic algorithm produces competitive Pareto fronts according to the applied convergence metric and it clearly outperforms the other three algorithms.

Abstract: In this paper, a memetic meta-heuristic called the shuffled frog-leaping algorithm (SFLA) is presented for solving the combinatorial optimisation problem associated with lay-up sequence optimisation of laminate composite structures The SFLA is a meta-heuristic optimization method that mimics the memetic evolution of a group of frogs when seeking for the location that has the maximum amount of available food The proposed SFLA algorithm is a hybrid version of the popular version We have incorporated a customized neighbourhood search algorithm and an adaptive search factor to accelerate the convergence characteristics Apart from this, a crossover operator being popularly used in evolutionary computing is suitably incorporated in the proposed hybrid version of SFLA The resulting hybrid SFL 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 hybrid SFL algorithm for solving combinatorial problems is demonstrated by solving a hybrid laminate composite cylindrical skirt problem subjected to both combinatorial as well as design constraints Further, the proposed algorithm is compared with three state-of-the-art multi-objective optimizers: Non-dominated sorting Genetic Algorithm (NSGA-II), Pareto Archived Evolutionary Strategy (PAES) and micro genetic algorithm (MGA) The studies presented in this paper indicate that proposed memetic algorithm produces competitive Pareto fronts according to the applied convergence metric and it clearly outperforms the other three algorithms