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By Edited by Eisuke Kita

ISBN-10: 9533071710

ISBN-13: 9789533071718

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These methods augment the cost function, so that it includes the squared or absolute values of the constraint violations multiplied by penalty coefficients. However, there are also serious drawbacks with penalty function methods. For example, small values of the penalty coefficients drive the search outside the feasible region and often produce infeasible solutions [29], if imposing very severe penalties makes it difficult to drive the population to the optimum [29-31]. To overcome these drawbacks, Kim and Myung [26] proposed the concept of two phase evolutionary algorithm, where the penalty method is implemented in the first phase, while during the second phase an augmented Lagrangian function is applied on the best solution of the first phase.

2007). A new self-adaptive crossover operator for real-coded evolutionary algorithms. Lecture Notes in Computer Science, 4431(1):39-48. (2004). A roubst evolutionary algorithm for constrained multiobjective optimization problems. Computer Research and Development,41(6): 986990. (2001). Multiobjective optimization using a Pareto differential evolution approach. Proceedings of 2002 World Congress on Computational Intelligence. Piscataway: IEEE Press, 1145-1150. (2000). Stochastic ranking for constrained evolutionary optimization.

After this point is reached the best results were overtaken by the HSA-EA with the random initialization procedure (Init). In contrary, the best results by the instances the nearest to the threshold according to the AES was observed by the HSA-EA without local search heuristics (None). 008) was observed as well. After this point results of the HSA-EA without local search heuristics becomes worse. Conversely, the HSA-EA with random initialization procedure that was the worst by the instances before the turning point becomes the best after this.

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Evolutionary Algorithms by Edited by Eisuke Kita

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