Multiobjective Imperialist Competitive Algorithm for Solving Nonlinear Constrained Optimization Problems

Abstract

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 biobjective 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.

Penerapan SMES untuk Load Frequency Control pada PLTMH Menggunakan Ant Colony Optimization

“…Penerapan SMES sebagai pengontrol frekuensi pada sistem tenaga juga telah dibahas, seperti pada [7][8][9], dan [10] pada pembangkit listrik termal. Optimasi parameter SMES menggunakan metode cerdas telah dibahas pada ref [11], pada [12] menggunakan Fuzzy Logic, pada [13] menggunakan Cuckoo Search Algorithm, pada [14] menggunakan Imperialist Competitive Algorithm, dan [15][16] menggunakan Particle Swarm Optimization. Dari penelitian di atas dapat diketahui bahwa SMES dapat menjadi pilihan yang cocok untuk penyimpanan energi dan kompensator jika dikontrol dengan benar menggunakan metode cerdas.…”

A dynamic multiobjective evolutionary algorithm based on a dynamic evolutionary environment model

“…In recent years, researchers have designed many new ways to solve DMOPs on the basis of static algorithms [10,11,13,14,16,17,18,19,20,21], such as random initialization [12,25,26,18,17], hyper mutation [25,22,15,33], memory [25,26,29,30,36,23,41], and prediction [31,32,33,34,35,36,42,48,49].…”