swMATH ID: 8963
Software Authors: Enayatifar, Rasul; Yousefi, Moslem; Abdullah, Abdul Hanan; Darus, Amer Nordin
Description: MOICA: a novel multi-objective approach based on imperialist competitive algorithm A novel multi-objective evolutionary algorithm (MOEA) is developed based on imperialist competitive algorithm (ICA), a newly introduced evolutionary algorithm (EA). Fast non-dominated sorting and the Sigma method are employed for ranking the solutions. The algorithm is tested on six well-known test functions each of them incorporate a particular feature that may cause difficulty to MOEAs. The numerical results indicate that MOICA shows significantly higher efficiency in terms of accuracy and maintaining a diverse population of solutions when compared to the existing salient MOEAs, namely fast elitism non-dominated sorting genetic algorithm (NSGA-II) and multi-objective particle swarm optimization (MOPSO). Considering the computational time, the proposed algorithm is slightly faster than MOPSO and significantly outperforms NSGA-II.
Homepage: http://www.google.de/#sclient=psy&hl=de&source=hp&q=MOICA
Keywords: multi-objective imperialist competitive algorithm; multi-objective optimization; Pareto front
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Cited in: 1 Publication

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