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首页> 外文期刊>International Journal of Swarm Intelligence and Evolutionary Computation >Multiobjective Imperialist Competitive Algorithm for Solving Nonlinear Constrained Optimization Problems
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Multiobjective Imperialist Competitive Algorithm for Solving Nonlinear Constrained Optimization Problems

机译：求解非线性约束优化问题的多目标帝国主义竞争算法

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摘要

Nonlinear Constrained Optimization Problem (NCOP) has been arisen in a diverse range of sciences such as portfolio, economic management, airspace engineering and intelligence system etc. In this paper, a new multiobjective imperialist competitive algorithm for solving NCOP is proposed. First, we review some existing excellent algorithms for solving NOCP; then, the nonlinear constrained optimization problem is transformed into a bi objective optimization problem. Second, in order to improve the diversity of evolution country swarm and help the evolution country swarm to approach or land into the feasible region of the search space, three kinds of different methods of colony moving toward their relevant imperialist are given. Thirdly, the new operator for exchanging position of the imperialist and colony is given similar as a recombination operator in genetic algorithm to enrich the exploration and exploitation abilities of the proposed algorithm. Fourth, a local search method is also presented in order to accelerate the convergence speed. At last, the new approach is tested on thirteen well-known NP-hard nonlinear constrained optimization functions, and the experiment evidences suggest that the proposed method is robust, efficient, and generic when solving nonlinear constrained optimization problem. Compared with some other state of the art algorithms, the proposed algorithm has remarkable advantages in terms of the best, mean, and worst objective function value and the standard deviations.
机译：非线性约束优化问题（NCOP）已经出现在诸如投资组合，经济管理，空域工程和情报系统等广泛的科学领域。本文提出了一种新的解决NCOP的帝国主义多目标竞争算法。首先，我们回顾一些解决NOCP的优秀算法。然后，将非线性约束优化问题转化为双目标优化问题。其次，为了改善进化国家群的多样性并帮助进化国家群接近或登陆搜索空间的可行区域，提出了三种不同的殖民地向其帝国主义发展的方法。第三，与遗传算法中的重组算子相似，给出了新的帝国主义与殖民地交换者，以丰富所提算法的探索和开发能力。第四，还提出了一种局部搜索方法，以加快收敛速度​​。最后，对13种著名的NP-hard非线性约束优化函数进行了测试，实验结果表明，该方法在求解非线性约束优化问题时具有鲁棒性，高效性和通用性。与其他一些现有技术算法相比，该算法在最佳，均值和最差目标函数值以及标准偏差方面具有明显优势。

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