An efficient many-objective evolutionary algorithm for ecological optimization problems
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Ecological problems are becoming more and more important in the engineering society and their importance are expected to be increased in the next century. Many ecological problems have more than one objective function in nature which makes it difficult to solve such problems using traditional single objective optimization algorithms. In this paper, we propose a new evolutionary algorithm based on the NSGA-II algorithm to efficiently solve the many-objective ecological optimization problems. The proposed algorithm uses three schemes to enhance the performance of NSGA-II algorithm to solve Many-objective ecological optimization problems. A new efficient sorting method, smart archive and simple local search are used to speed up the solutions convergence process to the POF and enhance the diversity of the solutions. The proposed method is general and can be adapted to a wide range of ecological optimization problems. The proposed algorithm is compared with the state-of-the-art multi-objective optimization algorithms using five DTLZ test problems. The results show that our proposed algorithm significantly outperforms the other algorithms when the number of objective functions is high. © 2018, Universidad del Zulia. All rights reserved.