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It is a challenge to balance the convergence and the diversity in multi-objective optimization problems. In this paper, a new two-stage based evolutionary algorithm (MOEA/TS) is proposed, where the convergence and the diversity are handled in two independent phases. In the first stage, the convergence is accelerated by using the gradient information of constrained sub-problems. In the second stage, the diversity is improved by adopting the dominance based multi-objective evolutionary algorithm. The comparative experiments are presented in terms of two performance indicators for benchmark test problems. The results indicates that MOEA/TS has the competitive performance. [ABSTRACT FROM AUTHOR]

Estimation of distribution algorithm (EDA) is a stochastic optimization algorithm based on probability distribution model and has been widely applied in global optimization. However, the random sampling of Gaussian EDA (GEDA) usually suffers from the poor diversity and the premature convergence, which severely limits its performance. This paper analyzes the shortcomings of the random sampling and develops an efficient mixture sampling model (EMSM). EMSM can explore more promising regions and utilize the unsuccessful mutation vectors, which achieves a good tradeoff between the diversity and the convergence. Moreover, the feasibility analysis of EMSM is studied. A new GEDA variant named EMSM-EDA is developed, which combines EMSM with enhancing Gaussian estimation of distribution algorithm (EDA2). The experimental results on IEEE CEC2013 and IEEE CEC2014 test suites demonstrate that EMSM-EDA is efficient and competitive. [ABSTRACT FROM AUTHOR]

The key of multimodal optimization algorithms is to divide the population into multiple species, and each species searches for a global optimum. This article proposes a new differential evolution based on the Hill-Valley technique for multimodal optimization. This technique uses history information to classify individuals on the same peak as one species. Compared to other niching techniques, this technique is insensitive to parameters and does not waste additional computing resources. For each species, the evolutionary state recognition method is developed to judge the evolutionary state and the search strategy is designed according to the evolutionary state. In addition, the prediction mechanism is designed to save computing resources. The experimental results show that the performance of the proposed algorithm is better than other algorithms on the CEC2013 test set and the NESs test set. [ABSTRACT FROM AUTHOR]

Multifactorial Optimization (MFO) has attracted considerable attention in the community of evolutionary computation, which aims to deal with multiple optimization tasks simultaneously by information transfer. Unfortunately, information transfer may cause both positive and negative effects. To address this issue, this paper exploits an explicit multipopulation evolutionary framework (MPEF) to intelligently take advantage of positive information transfer and effectively reduce negative information transfer. In MPEF, each task possesses an independent population and has its random mating probability for exploiting the information of other tasks. Moreover, the random mating probability of each task is adjusted adaptively. The benefits of using MPEF are twofold. 1) Various well-developed search engines can be easily embedded into MPEF for solving the single task of multifactorial optimization problems. 2) The positive information transfer can be exploited. Meanwhile, negative information transfer can be prevented. A multifactorial evolutionary algorithm (named MFMP) is realized as an instance by embedding a well-designed search engine into MPEF. The experimental results on some MFO benchmark problems demonstrate the advantage of MFMP over some state-of-the-art algorithms. Moreover, MFMP is also successfully employed to solve the spread spectrum radar polyphase code design (SSRPCD) problem. [ABSTRACT FROM AUTHOR]