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.

An efficient multi-objective optimization algorithm based on swarm intelligence for engineering design

Abstract: As there is a growing interest in applications of multi-objective optimization methods to real-world problems, it is essential to develop efficient algorithms to achieve better performance in engineering design and resources optimization. An efficient algorithm for multi-objective optimization, based on swarm intelligence principles, is presented in this article. The proposed algorithm incorporates a Pareto dominance relation into particle swarm optimization (PSO). To create effective selection pressure among the non-dominated solutions, it uses a variable size external repository and crowding distance comparison operator.An efficient mutation strategy called elitist-mutation is also incorporated in the algorithm. This strategic mechanism effectively explores the feasible search space and speeds up the search for the true Pareto-optimal region. The proposed approach is tested on various benchmark problems taken from the literature and validated with standard performance measures by comparison with NSGA-II, one of the best multi-objective evolutionary algorithms available at present. It is then applied to three engineering design problems. The results obtained amply demonstrate that the proposed approach is efficient and is able to yield a wide spread of solutions with good coverage and convergence to true Pareto-optimal fronts.

Optimal Energy Management Strategy of a Plug-in Hybrid Electric Vehicle Based on a Particle Swarm Optimization Algorithm

Abstract: Plug-in hybrid electric vehicles (PHEVs) have been recognized as one of the most promising vehicle categories nowadays due to their low fuel consumption and reduced emissions. Energy management is critical for improving the performance of PHEVs. This paper proposes an energy management approach based on a particle swarm optimization (PSO) algorithm. The optimization objective is to minimize total energy cost (summation of oil and electricity) from vehicle utilization. A main drawback of optimal strategies is that they can hardly be used in real-time control. In order to solve this problem, a rule-based strategy containing three operation modes is proposed first, and then the PSO algorithm is implemented on four threshold values in the presented rule-based strategy. The proposed strategy has been verified by the US06 driving cycle under the MATLAB/Simulink software environment. Two different driving cycles are adopted to evaluate the generalization ability of the proposed strategy. Simulation results indicate that the proposed PSO-based energy management method can achieve better energy efficiency compared with traditional blended strategies. Online control performance of the proposed approach has been demonstrated through a driver-in-the-loop real-time experiment.

A new hybrid particle swarm and simulated annealing stochastic optimization method

Abstract: Display Omitted Development of a new hybrid PSO-SA optimization method.Numerical validation of the proposed method using a number of benchmark functions.Using three criteria for comparative work.Finding near optimum parameters of the proposed method.Application of the proposed algorithm in two engineering problems. A novel hybrid particle swarm and simulated annealing stochastic optimization method is proposed. The proposed hybrid method uses both PSO and SA in sequence and integrates the merits of good exploration capability of PSO and good local search properties of SA. Numerical simulation has been performed for selection of near optimum parameters of the method. The performance of this hybrid optimization technique was evaluated by comparing optimization results of thirty benchmark functions of different dimensions with those obtained by other numerical methods considering three criteria. These criteria were stability, average trial function evaluations for successful runs and the total average trial function evaluations considering both successful and failed runs. Design of laminated composite materials with required effective stiffness properties and minimum weight design of a three-bar truss are addressed as typical applications of the proposed algorithm in various types of optimization problems. In general, the proposed hybrid PSO-SA algorithm demonstrates improved performance in solution of these problems compared to other evolutionary methods The results of this research show that the proposed algorithm can reliably and effectively be used for various optimization problems.

Applying Family Competition to Evolution Strategies for Constrained Optimization

Abstract: This paper applies family competition to evolution strategies to solve constrained optimization problems. The family competition of Family Competition Evolution Strategy (FCES) can be viewed as a local competition involving the children generated from the same parent, while the selection is a global competition among all of the members in the population. According to our experimental results, the self-adaptation of strategy parameters with deterministic elitist selection may trap ESs into local optima when they are applied to heavy constrained optimization problems. By controlling strategy parameters with non-self adaptive rule, FCES can reduce the computation time of self-adaptive Gaussian mutation, diminish the complexity of selection from (m+1) to (m+m), and avoid to be premature. Therefore, FCES is capable of obtaining better performance and saving the computation time. In this paper, FCES is compared with other evolutionary algorithms on various benchmark problems and the results indicate that FCES is a powerful optimization technique.