Solving Constrained Optimization Problems by an Improved Particle Swarm Optimization (original) (raw)
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An improved vector particle swarm optimization for constrained optimization problems
Information Sciences, 2011
Increasing attention is being paid to solve constrained optimization problems (COP) frequently encountered in real-world applications. In this paper, an improved vector particle swarm optimization (IVPSO) algorithm is proposed to solve COPs. The constraint-handling technique is based on the simple constraint-preserving method. Velocity and position of each particle, as well as the corresponding changes, are all expressed as vectors in order to present the optimization procedure in a more intuitively comprehensible manner. The NVPSO algorithm [30], which uses one-dimensional search approaches to find a new feasible position on the flying trajectory of the particle when it escapes from the feasible region, has been proposed to solve COP. Experimental results showed that searching only on the flying trajectory for a feasible position influenced the diversity of the swarm and thus reduced the global search capability of the NVPSO algorithm. In order to avoid neglecting any worthy position in the feasible region and improve the optimization efficiency, a multi-dimensional search algorithm is proposed to search within a local region for a new feasible position. The local region is composed of all dimensions of the escaped particle's parent and the current positions. Obviously, the flying trajectory of the particle is also included in this local region. The new position is not only present in the feasible region but also has a better fitness value in this local region. The performance of IVPSO is tested on 13 well-known benchmark functions. Experimental results prove that the proposed IVPSO algorithm is simple, competitive and stable.
Self-adaptive mix of particle swarm methodologies for constrained optimization
Information Sciences, 2014
In recent years, many different variants of the particle swarm optimizer (PSO) for solving optimization problems have been proposed. However, PSO has an inherent drawback in handling constrained problems, mainly because of its complexity and dependency on parameters. Furthermore, one PSO variant may perform well for some test problems but not obtain good results for others. In this paper, our purpose is to develop a new PSO algorithm that can efficiently solve a variety of constrained optimization problems. It considers a mix of different PSO variants each of which evolves with a different number of individuals from the current population. In each generation, the algorithm assigns more individuals to the better-performing variants and fewer to the worse-performing ones. Also, a new PSO variant is developed for use in the proposed algorithm to maintain a better balance between its local and global PSO versions. A new methodology for adapting PSO parameters is presented and the proposed self-adaptive PSO algorithm tested and analyzed on two sets of test problems, namely the CEC2006 and CEC2010 constrained optimization problems. Based on the results, the proposed algorithm shows significantly better performance than the same global and local PSO variants as well as other-state-of-the-art algorithms. Although, based on our analysis, it cannot guarantee an optimal solution for any unknown problem, it is expected to be able to solve a wide variety of practical problems.
Constrained optimization via particle evolutionary swarm optimization algorithm (PESO
2005
We introduce the PESO (Particle Evolutionary Swarm Optimization) algorithm for solving single objective constrained optimization problems. PESO algorithm proposes two new perturbation operators: "c-perturbation" and "mperturbation". The goal of these operators is to fight premature convergence and poor diversity issues observed in Particle Swarm Optimization (PSO) implementations. Constraint handling is based on simple feasibility rules. PESO is compared with respect to a highly competitive technique representative of the state-of-the-art in the area using a well-known benchmark for evolutionary constrained optimization. PESO matches mosts results and outperforms other PSO algorithms.
Constraint Handling in Particle Swarm Optimization
Innovations and Developments of Swarm Intelligence Applications
In this article, the authors propose a particle swarm optimization (PSO) for constrained optimization. The proposed PSO adopts a multiobjective approach to constraint handling. Procedures to update the feasible and infeasible personal best are designed to encourage finding feasible regions and convergence toward the Pareto front. In addition, the infeasible nondominated solutions are stored in the global best archive to exploit the hidden information for guiding the particles toward feasible regions. Furthermore, the number of feasible personal best in the personal best memory and the scalar constraint violations of personal best and global best are used to adapt the acceleration constants in the PSO flight equations. The purpose is to find more feasible particles and search for better solutions during the process. The mutation procedure is applied to encourage global and fine-tune local searches. The simulation results indicate that the proposed constrained PSO is highly competitiv...
Constrained self regulating particle swarm optimization
Bulletin of Electrical Engineering and Informatics, 2022
Self regulating particle swarm optimization (SRPSO) is a variant of particle swarm optimization (PSO) which has proved to be a very efficient algorithm for unconstrained optimization compared with other evolutionary algorithms (EAs) and utilized recently by the researchers for solving real-world problems. However, SRPSO has not been evaluated and analyzed for constrained optimization. In this work, SRPSO has been evaluated exhaustively for constrained optimization using the 24 constrained benchmark problems by coupling it with four efficient constraint handling techniques (CHTs). The results of constrained SRPSO algorithm have been compared with two other algorithms i.e. Differential evolution (DE) and PSO. DE and PSO have also been coupled with same four CHTs and evaluated on the 24 constrained benchmark problems. Statistical analysis on performance evaluation of three algorithms on the benchmark problems shows that constrained SRPSO algorithm performance is better than constrained PSO but it is found to be deficient when compared with constrained DE with 95% confidence level. Therefore, the objective of this work is to evaluate the SRPSO algorithm comprehensively for constrained optimization with different views to come up with suitability of constrained SRPSO algorithm when coupled with particular CHT for solving specific type of problems.
Improved Particle Swarm Optimization in Constrained Numerical Search Spaces
Studies in Computational Intelligence, 2009
This chapter presents a study about the behavior of Particle Swarm Optimization (PSO) in constrained search spaces. A comparison of four well-known PSO variants used to solve a set of test problems is presented. Based on the information obtained, the most competitive PSO variant is detected. From this preliminary analysis, the performance of this variant is improved with two simple modifications related with the dynamic control of some parameters and a variation in the constraint-handling technique. These changes keep the simplicity of PSO i.e. no extra parameters, mechanisms controlled by the user or combination of PSO variants are added. This Improved PSO (IPSO) is extensively compared against the original PSO variants, based on the quality and consistency of the final results and also on two performance measures and convergence graphs to analyze their on-line behavior. Finally, IPSO is compared against some state-of-the-art PSO-based approaches for constrained optimization. Statistical tests are used in the experiments in order to add support to the findings and conclusions established.
Particle Swarm Optimization for Solving Nonlinear Programming Problems
In the beginning we provide a brief introduction to the basic concepts of optimization and global optimization, evolutionary computation and swarm intelligence. The necessity of solving optimization problems is outlined and various types of optimization problems are discussed. A rough classification of established optimization algorithms is provided, followed by Particle Swarm Optimization (PSO) and different types of PSO. Change in velocity component using velocity clamping techniques by bisection method and golden search method are discussed. We have discussed advantages of Using Self-Accelerated Smart Particle Swarm Optimization (SAS-PSO) technique which was introduced . Finally, the numerical values of the objective function are calculated which are optimal solution for the problem. The SAS-PSO and Standard Particle Swarm Optimization technique is compared as a result SAS-PSO does not require any additional parameter like acceleration co-efficient and inertia-weight as in case of other standard PSO algorithms.
2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), 2008
In this paper, we propose a novel approach to solve constrained optimization problems based on particle swarm optimization (PSO). First, an empirical comparison of the most popular PSO variants is presented as to select the most convenient among them. After that, the PSO variant chosen is improved in: (1) its parameter control with a dynamic proposal as to promote a better exploration of the search space and to avoid premature convergence and (2) its constraint-handling mechanism by using multiobjective concepts as to promote a better approach to the feasible region. The algorithm is tested on a set of 13 well-known benchmark problems and the obtained performance is compared against some PSO variants and stateof-the-art approaches. Based on the results presented some conclusions are drawn and the future work is established.
Constraint-Handling Techniques for Particle Swarm Optimization Algorithms
ArXiv, 2021
Population-based methods such as Evolutionary Algorithms (EAs) and Particle Swarm Optimization (PSO) have proven their ability to cope with a variety of remarkably different problems, regardless of whether they are or are not linear, convex, differentiable or smooth. In addition, they are able to handle problems of notably higher complexity than traditional methods. The main procedure consists of successively updating a population of candidate solutions, performing a parallel exploration instead of traditional sequential exploration (usually unable to overcome local pathologies). While the origins of the PSO method are linked to bird flock simulations, it is a stochastic optimization method in the sense that it relies on random coefficients to introduce creativity, and a bottom-up artificial intelligence-based approach in the sense that its intelligent behaviour emerges in a higher level than the individuals’ rather than deterministically programmed. As opposed to EAs, the PSO invol...