Unified Particle Swarm Optimization Algorithm Research Articles

Multi-swarm UPSO algorithm based on seed strategy for atomic clusters structure optimization

Particle Swarm Optimization (PSO) algorithm is prone to get trapped in local optima and insufficient information exchange among particles. To solve this problem, this paper proposes a Multi-swarm Unified Particle Swarm Optimization algorithm based on Seed Strategy (SS-DMS-UPSO) to optimize the atomic clusters structure. In this algorithm, the population is divided into some sub-populations evolving randomly and evenly, and each sub-population uses UPSO algorithm with different unification factors to evolve independently in parallel. After a certain number of independent evolution, the particles of all sub-populations are merged into a new population, and the population is again randomly divided into average sub-populations. Iterate the algorithm repeatedly in this way. And finally the global best particle can be obtained. The experimental results show that the SS-DMS-UPSO algorithm can search for the optimal structure or extremely similar optimal structure for atomic clusters with atomic numbers between 2 and 31. For atomic clusters with atomic numbers between 32 and 35, the algorithm can find its approximate optimal structure. Compared with other algorithms, the difference between the lowest energy value and the ideal energy value obtained by the SS-DMS-UPSO algorithm is much smaller. It means that its optimal structure of the atomic clusters is closer to the stable structure, and the algorithm is more stable, which proves the effectiveness of the SS-DMS-UPSO algorithm.

Tailoring Job Shop Scheduling Problem Instances Through Unified Particle Swarm Optimization

Problem instances are paramount when testing the performance of any learning algorithm. For this reason, it is customary to use widespread problems known as benchmark instances. Nonetheless, these are usually generated disregarding heuristics and their nature. For Job Shop Scheduling problem, researchers have created such instances based on random distributions. This idea may bias conclusions about the algorithm under test since a practitioner can only observe performance from a limited perspective, which may not even reflect real-life situations. However, addressing this issue implies tackling the instance generation problem while considering the nature of the solution approach. Hence, in this work, we propose an instance generator based on the Unified Particle Swarm Optimization algorithm, which can tailor instances to different goals. To validate our approach, we include instances generated to different heuristics and instances tailored to a variety of features. In the first case, we seek to favor or hinder one heuristic whereas doing the opposite for the remaining ones. In the second one, we explore instances with specific feature values. Our data reveal that the proposed approach fulfills the expectations and can effectively deal with different kinds of instances. We analyse the nature of the generated instances and their insights, which can be used to further the study about heuristics and problem features.

Buckling load enhancement of damaged composite plates under hygrothermal environment using unified particle swarm optimization

Optimization with Unified Particle Swarm Optimization (UPSO) method is performed for the enhancement of buckling load capacity of composite plates having damage under hygrothermal environment which has received little or no attention in the literature. Numerical results are presented for effect of damage in buckling behavior of laminated composite plates using an anisotropic damage model. Optimized critical buckling temperature of laminated plates with internal flaw is computed with the fiber orientation as the design variable by employing a UPSO algorithm and results are compared with undamaged case for various aspect ratios, ply orientations, and boundary conditions. FEM formulation and programming in the MATLAB environment have been performed. The results of this work will assist designers to address some key issues concerning composite structures. It is observed that the degradation of buckling strength of a structural element in hygrothermal environment as a result of internal flaws can be avoided to a large extent if we use these optimized ply orientations at design phase of the composite structure. This specific application proves the contribution of present work to be of realistic nature.

An optimal high thermal conductive graphite microchannel for electronic device cooling

This article describes the design of an optimal rectangular microchannel made of a high thermal conductive graphite (HTCG). For simulating the proposed microchannel heat sink, the total resistance model and the entropy generation minimization criterion were used. For solving the optimization problem, the unifi ed particle swarm optimization algorithm (UPSO), was used. Results showed a marked effect of using this high thermal conductor when compared to traditional materials, such as aluminum, and while using air and ammonia gas as the working fl uids. It is also reported the relative effect of the constriction, convective and fl uid thermal resistances on the overall equivalent thermal resistance. As a demonstrative example when changing the nature of the coolant, a titanium dioxide nanofl uid was selected. It was found that the Nusselt number is perceptibly lower, when the coolant is a nanofl uid and the material for the making of the microchannel is an HTCG.

Suppression of Chaotic Behavior in Duffing-holmes System using Back-stepping Controller Optimized by Unified Particle Swarm Optimization Algorithm

The nonlinear behavior analysis and chaos control for Duffing-Holmes chaotic system is discussed in this paper. In order to suppress the irregular chaotic motion, an optimal back-stepping controller is designed. The back-stepping method consists of parameters with positive values. The improper selection of the parameters leads to inappropriate responses or even may lead to instability of the system. In this paper, the Unified particle swarm optimization (UPSO) algorithm is utilized to determine the convenient and optimal values of the parameters. The minimized objective function via UPSO algorithm is a weighted sum of the Integral of Time multiplied Absolute Error (ITAE) and squared control signal. Fast control of chaos in a very short time and having more limited control signal for this purpose, are the great advantages of the proposed controller. Numerical simulations show the high performance of this method for chaos elimination in Duffing-Holmes system.