Balancing the convergence and diversity of many-objective evolutionary algorithms is difficult and challenging. In this work, a multi-search strategy based on decomposition is proposed to generate good offspring and improve convergence, and an external population strategy is used to maintain the diversity of the obtained solutions. The multi-search strategy allows the selection of sparse and convergent nondominated solutions to carry out the exploration and exploitation steps. Experiments are conducted on 15 benchmark functions from the CEC 2018 with 5, 10, and 15 objectives. The results indicate that the proposed algorithm can obtain a set of solutions with better diversity and convergence than the five efficient state-of-the-art algorithms, i.e. NSGAIII, MOEA/D, MOEA/DD, KnEA, and RVEA.

The diversity and convergence of obtained solutions are two main goals for multi-objective evolutionary algorithms. In this paper, a new decomposition based evolutionary algorithm with uniform design (MOEA/DU) is designed to achieve these two goals. Firstly, the objective space of a multi-objective problem is decomposed into a set of sub-regions based on a set of direction vectors, and each sub-region is made to have a solution for maintaining the diversity. Secondly, for domination solutions, a selection strategy and a crossover operator based on uniform design are used to make these solutions as soon as possibly become non-domination solutions. The proposed algorithm has been compared with NSGAII, MOEA/D and MOEA/D-M2M on seven test instances. The experimental results illustrate that the proposed algorithm is able to find a set of solutions with better diversity and convergence.

Research on multiobjective optimization problems becomes one of the hottest topics of intelligent computation. In order to improve the search efficiency of an evolutionary algorithm and maintain the diversity of solutions, in this paper, the learning automata (LA) is first used for quantization orthogonal crossover (QOX), and a new fitness function based on decomposition is proposed to achieve these two purposes. Based on these, an orthogonal evolutionary algorithm with LA for complex multiobjective optimization problems with continuous variables is proposed. The experimental results show that in continuous states, the proposed algorithm is able to achieve accurate Pareto-optimal sets and wide Pareto-optimal fronts efficiently. Moreover, the comparison with the several existing well-known algorithms: nondominated sorting genetic algorithm II, decomposition-based multiobjective evolutionary algorithm, decomposition-based multiobjective evolutionary algorithm with an ensemble of neighborhood sizes, multiobjective optimization by LA, and multiobjective immune algorithm with nondominated neighbor-based selection, on 15 multiobjective benchmark problems, shows that the proposed algorithm is able to find more accurate and evenly distributed Pareto-optimal fronts than the compared ones.

Inspired by the fact that the division of labor and cooperation play extremely important roles in the human history development, this paper develops a novel artificial bee colony algorithm based on information learning (ILABC, for short). In ILABC, at each generation, the whole population is divided into several subpopulations by the clustering partition and the size of subpopulation is dynamically adjusted based on the last search experience, which results in a clear division of labor. Furthermore, the two search mechanisms are designed to facilitate the exchange of information in each subpopulation and between different subpopulations, respectively, which acts as the cooperation. Finally, the comparison results on a number of benchmark functions demonstrate that the proposed method performs competitively and effectively when compared to the selected state-of-the-art algorithms.