A novel strategy of Pareto-optimal solution searching in multi-objective particle swarm optimization (MOPSO).

*(English)*Zbl 1186.90108Summary: In multi-objective particle swarm optimization (MOPSO) algorithms, finding the global optimal particle \((gBest)\) for each particle of the swarm from a set of non-dominated solutions is very difficult yet an important problem for attaining convergence and diversity of solutions. First, a new Pareto-optimal solution searching algorithm for finding the \(gBest\) in MOPSO is introduced in this paper, which can compromise global and local searching based on the process of evolution. The algorithm is implemented and is compared with another algorithm which uses the Sigma method for finding \(gBest\) on a set of well-designed test functions. Finally, the multi-objective optimal regulation of cascade reservoirs is successfully solved by the proposed algorithm.

##### MSC:

90C29 | Multi-objective and goal programming |

90C59 | Approximation methods and heuristics in mathematical programming |

##### Keywords:

evolutionary algorithms; multiple objectives; particle swarm optimization; optimal regulation of cascade reservoirs
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\textit{J. Yang} et al., Comput. Math. Appl. 57, No. 11--12, 1995--2000 (2009; Zbl 1186.90108)

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##### References:

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