Pure Particle Swarm Optimization Research Articles

Hybrid Intelligent Dynamic Optimization of Switched Systems

In this article, a hybrid intelligent dynamic optimization method integrating of particle swarm optimization (PSO) and gradient-based optimization (GBO) is proposed for optimal control of switched systems. The method both improves the solution accuracy of PSO and avoids falling into local minima generated by the deterministic optimization. First, the PSO algorithm with ring topology is used to explore the whole search space to detect the global optimum area. Secondly, the GBO algorithm is deployed in the detected global optimum area to achieve faster convergence rate and higher precision solution than those of pure PSO. Finally, the simulation results show that the algorithm outperforms both PSO and GBO in terms of solution accuracy and computational cost.

Hybrid particle swarm optimization and tabu search for the design of large-scale water distribution networks

ABSTRACT Water distribution network (WDN) optimization has received special attention from various technicians and researchers, mainly due to its high costs of implementation, operation and maintenance. However, the low computational efficiency of most developed algorithms makes them difficult to apply in large-scale WDN design problems. This article presents a hybrid particle swarm optimization and tabu search (H-PSOTS) algorithm for WDN design. Incorporating tabu search (TS) as a local improvement procedure enables the H-PSOTS algorithm to avoid local optima and show satisfactory performance. Pure particle swarm optimization (PSO) and H-PSOTS algorithms were applied to three benchmark networks proposed in the literature: the Balerma irrigation network, the ZJ network and the Rural network. The hybrid methodology obtained good results when seeking an optimal solution and revealed high computational performance, making it a new option for the optimal design of real water distribution networks.

Hybrid PSO-ACO technique to solve multi-constraint economic load dispatch problems for 6-generator system

This paper presents a new hybrid soft computing approach in solving typical economic load dispatch (ELD) problem by combining two widely used meta-heuristic techniques, namely particle swarm optimization (PSO) and ant colony optimization (ACO). Though PSO has been popularly used in ELD problems for its flexibility, robustness, and fast convergence, it often produces not so good solution due to premature convergence to local optima. ACO, on the other hand, popular for its good global exploration feature, strikes a fine balance between local and global search for optimum solution when combined with PSO. This aspect has been exploited for the first time by applying a customized combination of PSO and ACO in finding solution for both convex and non-convex ELD problems with smooth and non-smooth cost functions (i.e. with and without valve-point loading) and multiple constraints like generator capacity, transmission loss, ramp rate limits, and prohibited operating zones. This paper showcases the results of different test cases for the proposed approach implemented in a simulated 6-generator thermal power system; the results validate the effectiveness and superiority of PSO–ACO when compared with few other hybrid approaches and pure PSO and ACO approaches under similar test conditions. Considering the quality of solution including convergence characteristics, power loss, and total generation cost, PSO–ACO emerges as a viable alternative for solving ELD problems in a real power generation scenario.

Kinetic parameters estimation of protease production using penalty function method with hybrid genetic algorithm and particle swarm optimization

ABSTRACTAlmost all optimization techniques are restricted by the problems' dimensions and large search spaces. This research focuses on a special hybrid method combining two meta-heuristic techniques, genetic algorithms (GA) and particle swarm optimization (PSO) that aims at overcoming this issue. This method investigates the potential impact of constraints (or the feasible regions) on the search pattern of GA and PSO. The proposed algorithm was applied for parameter estimation of batch fermentation process for alkaline protease production by Bacillus licheniformis in submerged culture. Furthermore, a comparison of proposed hybrid GA/PSO with pure GA and pure PSO was carried out. The results revealed that combination of these two meta-heuristic algorithms speeds up the search (about two-fold faster) in comparison to pure algorithms, since it benefits from synergy. Hence, the proposed method can be considered as an applicable method for parameter estimation of biological models in particular for large search space problems. Also, it was concluded that PSO has a slightly better performance and possesses better convergence and computational time than GA.

Hybrid gradient particle swarm optimization for dynamic optimization problems of chemical processes

ABSTRACTDynamic optimization problems (DOP) in chemical processes are very challenging because of their highly nonlinear, multidimensional, multipeak and constrained nature. In this paper, we propose a novel algorithm named hybrid gradient particle swarm optimization (HGPSO) by hybridizing particle swarm optimization (PSO) with gradient‐based algorithms (GBA). HGSPO can improve the convergence rate and solution precision of pure PSO, and avoid getting trapped to local optimums with pure GBA search. We further incorporate HGPSO into control vector parameterization (CVP), a method converting DOP into nonlinear programming, to solve five complex DOPs. These DOPs include multimodal, multidimensional and constrained problems. The experiments demonstrate that HGPSO performs much better in terms of solution precision and computational cost when compared with other PSO variants. © 2013 Curtin University of Technology and John Wiley & Sons, Ltd.

A PSO-based adaptive fuzzy PID-controllers

In this paper, a novel design method for determining the optimal fuzzy PID-controller parameters of active automobile suspension system using the particle swarm optimization (PSO) reinforcement evolutionary algorithm is presented. This paper demonstrated in detail how to help the PSO with Q-learning cooperation method to search efficiently the optimal fuzzy-PID controller parameters of a suspension system. The design of a fuzzy system can be formulated as a search problem in high-dimensional space where each point represents a rule set, membership functions, and the corresponding system’s behavior. In order to avoid obtaining the local optimum solution, we adopted a pure PSO global exploration method to search fuzzy-PID parameter. Later this paper explored the improved the limitation between suspension and tire deflection in active automobile suspension system with nonlinearity, which needs to be solved ride comfort and road holding ability problems, and so on. These studies presented many ideas to solve these existing problems, but they need much evolution time to obtain the solution. Motivated by above discussions this paper propose a novel algorithm which can decrease the number of evolution generation, and can also evolve the fuzzy system for obtaining a better performance.

Application of a hybrid of particle swarm and genetic algorithm for structural damage detection

This study presents a novel optimization algorithm which is a hybrid of particle swarm optimization (PSO) method and genetic algorithm (GA). Using the Ackley and Schwefel multimodal benchmark functions incorporating up to 25 variables, the performance of the hybrid is compared with pure PSO and GA and found to be far superior in convergence and accuracy. The hybrid algorithm is then used to identify multiple crack damages in a thin plate using an inverse time-domain formulation. The damage is detected using an orthotropic finite element (FE) model based on the strain energy equivalence principle. The identification is carried out using time-domain acceleration responses. The principle is to minimize the difference between the measured and theoretically predicted accelerations. Since the computational effort of identifying the use of global FE model proved prohibitive, a quarter substructure was identified which contains 72 damage variables. Using numerically simulated experiments, three cracks in a plate were reliably detected using this method in the presence of noise. While the pure particle swarm algorithm proved to be fast, the hybrid algorithm proved to be more accurate in damage prediction. GA performed worst in speed and accuracy.

Structural System Identification in the Time Domain using Evolutionary and Behaviorally Inspired Algorithms and their Hybrids

In this study, parametric identification of structural properties such as stiffness and damping is carried out using acceleration responses in the time domain. The process consists of minimizing the difference between the experimentally measured and theoretically predicted acceleration responses. The unknown parameters of certain numerical models, viz., a ten degree of freedom lumped mass system, a nine member truss and a non-uniform simply supported beam are thus identified. Evolutionary and behaviorally inspired optimization algorithms are used for minimization operations. The performance of their hybrid combinations is also investigated. Genetic Algorithm (GA) is a well known evolutionary algorithm used in system identification. Recently Particle Swarm Optimization (PSO), a behaviorally inspired algorithm, has emerged as a strong contender to GA in speed and accuracy. The discrete Ant Colony Optimization (ACO) method is yet another behaviorally inspired method studied here. The performance (speed and accuracy) of each algorithm alone and in their hybrid combinations such as GA with PSO, ACO with PSO and ACO with GA are extensively investigated using the numerical examples with effects of noise added for realism. The GA+PSO hybrid algorithm was found to give the best performance in speed and accuracy compared to all others. The next best in performance was pure PSO followed by pure GA. ACO performed poorly in all the cases.

Application of hybrid evolutionary algorithms to low exhaust emission diesel engine design

A hybrid evolutionary algorithm, consisting of a genetic algorithm (GA) and particle swarm optimization (PSO), is proposed. Generally, GAs maintain diverse solutions of good quality in multi-objective problems, while PSO shows fast convergence to the optimum solution. By coupling these algorithms, GA will compensate for the low diversity of PSO, while PSO will compensate for the high computational costs of GA. The hybrid algorithm was validated using standard test functions. The results showed that the hybrid algorithm has better performance than either a pure GA or pure PSO. The method was applied to an engineering design problem—the geometry of diesel engine combustion chamber reducing exhaust emissions such as NOx, soot and CO was optimized. The results demonstrated the usefulness of the present method to this engineering design problem. To identify the relation between exhaust emissions and combustion chamber geometry, data mining was performed with a self-organising map (SOM). The results indicate that the volume near the lower central part of the combustion chamber has a large effect on exhaust emissions and the optimum chamber geometry will vary depending on fuel injection angle.

An effective hybrid PSOSA strategy for optimization and its application to parameter estimation

As a novel evolutionary technique, particle swarm optimization (PSO) has received increasing attention and wide applications in a variety of fields. In this paper, an effective hybrid optimization strategy by incorporating the jumping property of simulated annealing (SA) into PSO, namely PSOSA, is proposed for estimating parameters of non-linear systems, which is an important issue in control fields and essentially is a hard multi-dimensional numerical optimization problem. By employing the SA-based selection for the best position when updating the velocity in PSO, the hybrid strategy is of more effective global exploration ability over pure PSO at the beginning searching stage (when temperature is high) so as to avoid premature convergence. As the temperature decreases, the hybrid strategy transforms to PSO smoothly to stress the exploitation. Simulation results based on three different kinds of models as well as a DTS200 three-tank system demonstrate the effectiveness and efficiency of the proposed PSOSA hybrid strategy, whose estimating quality is much better than that resulted by GA and is competitive to that resulted by GASA and SMSA hybrid strategies. Moreover, it is also demonstrated by comparative simulation results that, the ability to avoid being trapped in local optimum of PSOSA is much superior to pure PSO.