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.

Parallel Particle Swarm Optimization Algorithm Accelerated by Asynchronous Evaluations

Abstract: A parallel Particle Swarm Optimization (PSO) algorithm is presented. Particle swarm optimization is a fairly recent addition to the family of non-gradient based, probabilistic search algorithms that is based on a simplified social model and is closely tied to swarming theory. Although PSO algorithms present several attractive properties to the designer, they are plagued by high computational cost as measured by elapsed time. One approach to reduce the elapsed time is to make use of coarse-grained parallelization to evaluate the design points. Previous parallel PSO algorithms were mostly implemented in a synchronous manner, where all design points within a design iteration are evaluated before the next iteration is started. This approach leads to poor parallel speedup in cases where a heterogeneous parallel environment is used and/or where the analysis time depends on the design point being analyzed. This paper introduces an asynchronous parallel PSO algorithm that greatly improves the parallel e ciency. The asynchronous algorithm is benchmarked on a cluster assembled of Apple Macintosh G5 desktop computers, using the multi-disciplinary optimization of a typical transport aircraft wing as an example.

Gaussian swarm: a novel particle swarm optimization algorithm

Abstract: In this paper, a novel particle swarm optimization algorithm based on the Gaussian probability distribution is proposed. The standard particle swarm optimization (PSO) algorithm has some parameters that need to be specified before using the algorithm, e.g., the accelerating constants c/sub 1/ and c/sub 2/, the inertia weight w, the maximum velocity V/sub max/, and the number of particles of the swarm. The purpose of this work is the development of an algorithm based on the Gaussian distribution, which improves the convergence ability of PSO without the necessity of tuning these parameters. The only parameter to be specified by the user is the number of particles. The Gaussian PSO algorithm was tested on a suite of well-known benchmark functions and the results were compared with the results of the standard PSO algorithm. The simulation results shows that the Gaussian swarm outperforms the standard one.

Differential Evolution (DEoptim) for Non-Convex Portfolio Optimization

Abstract: The R package DEoptim implements the Differential Evolution algorithm. This algorithm is an evolutionary technique similar to classic genetic algorithms that is useful for the solution of global optimization problems. In this note we provide an introduction to the package and demonstrate its utility for financial applications by solving a non-convex portfolio optimization problem.

Real-Coded Chemical Reaction Optimization

Abstract: Optimization problems can generally be classified as continuous and discrete, based on the nature of the solution space. A recently developed chemical-reaction-inspired metaheuristic, called chemical reaction optimization (CRO), has been shown to perform well in many optimization problems in the discrete domain. This paper is dedicated to proposing a real-coded version of CRO, namely, RCCRO, to solve continuous optimization problems. We compare the performance of RCCRO with a large number of optimization techniques on a large set of standard continuous benchmark functions. We find that RCCRO outperforms all the others on the average. We also propose an adaptive scheme for RCCRO which can improve the performance effectively. This shows that CRO is suitable for solving problems in the continuous domain.