Multi-swarm optimization

About: Multi-swarm optimization is a research topic. Over the lifetime, 19162 publications have been published within this topic receiving 549725 citations.

Co-evolutionary particle swarm optimization to solve min-max problems

Abstract: A co-evolutionary particle swarm optimization (PSO) to solve constrained optimization problems is proposed. First, we introduce the augmented Lagrangian to transform a constrained optimization to a min-max problem with the saddle-point solution. Next, a co-evolutionary PSO algorithm is developed with one PSO focusing on the minimum part of the min-max problem with the other PSO focusing on the maximum part of the min-max problem. The two PSOs are connected through the fitness function. In the fitness calculation of one PSO, the other PSO serves as the environment to that PSO. The new algorithm is tested on three benchmark functions. The simulation results illustrate the efficiency and effectiveness of the new co-evolutionary particle swarm algorithm.

A new hybrid particle swarm optimization approach for structural design optimization in the automotive industry

Abstract: This paper presents an innovative optimization approach to solve structural design optimization problems in the automotive industry. The new approach is based on Taguchi’s robust design approach and particle swarm optimization algorithm. The proposed approach is applied to the structural design optimization of a vehicle part to illustrate how the present approach can be applied for solving design optimization problems. The results show the ability of the proposed approach to find better optimal solutions for structural design optimization problems.

Application of particle swarm optimization algorithm for solving bi-level linear programming problem

Abstract: Bi-level linear programming is a technique for modeling decentralized decision. It consists of the upper-level and lower-level objectives. This paper attempts to develop an efficient method based on particle swarm optimization (PSO) algorithm with swarm intelligence. The performance of the proposed method is ascertained by comparing the results with genetic algorithm (GA) using four problems in the literature and an example of supply chain model. The results illustrate that the PSO algorithm outperforms GA in accuracy.

Optimal Contraction Theorem for Exploration–Exploitation Tradeoff in Search and Optimization

Abstract: Global optimization process can often be divided into two subprocesses: exploration and exploitation. The tradeoff between exploration and exploitation (T:Er&Ei) is crucial in search and optimization, having a great effect on global optimization performance, e.g., accuracy and convergence speed of optimization algorithms. In this paper, definitions of exploration and exploitation are first given based on information correlation among samplings. Then, some general indicators of optimization hardness are presented to characterize problem difficulties. By analyzing a typical contraction-based three-stage optimization process, optimal contraction theorem is presented to show that T:Er&Ei depends on the optimization hardness of problems to be optimized. T:Er&Ei will gradually lean toward exploration as optimization hardness increases. In the case of great optimization hardness, exploration-dominated optimizers outperform exploitation-dominated optimizers. In particular, random sampling will become an outstanding optimizer when optimization hardness reaches a certain degree. Besides, the optimal number of contraction stages increases with optimization hardness. In an optimal contraction way, the whole sampling cost is evenly distributed in all contraction stages, and each contraction takes the same contracting ratio. Furthermore, the characterization of optimization hardness is discussed in detail. The experiments with several typical global optimization algorithms used to optimize three groups of test problems validate the correctness of the conclusions made by T:Er&Ei analysis.