Cao, Yijia; Cao, Lihua; Li, Yong; Xin, Jianbo An improved adaptive multiobjective particle swarm algorithm. (Chinese. English summary) Zbl 1324.68168 J. Hunan Univ., Nat. Sci. 41, No. 10, 84-90 (2014). Summary: Boundary handling and global best guider selection operators play important roles in the multiobjective particle swarm optimization (MOPSO) algorithm. Considering the characteristics of different operators, an improved adaptive MOPSO is proposed. When the algorithm falls into a local optimum, the crossover and mutation operators are initiated; when the convergence of algorithm hasn’t improved in a given duration, it switches the boundary handling operators between the truncation and the exponential distribution truncation; when the diversity of algorithm hasn’t improved in a given duration, it switches the global best guider selection operator between the probability inverse proportion to the crowding distance and the probability inverse proportion to the number of dominating solutions. The results of the benchmark functions and the optimal allocation problem of flexible AC transmission system (FACTS) devices confirm the effectiveness of the proposed algorithm. MSC: 68T20 Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) 90C29 Multi-objective and goal programming 90C59 Approximation methods and heuristics in mathematical programming 93C40 Adaptive control/observation systems Keywords:multiobjective optimization; particle swarm optimization; Pareto optimality; constrained domination; bound handling; global best selection; adaptive control; total transfer capability PDFBibTeX XMLCite \textit{Y. Cao} et al., J. Hunan Univ., Nat. Sci. 41, No. 10, 84--90 (2014; Zbl 1324.68168)