Hybrid PSO and GA for global maximization

Science Publishing Corporation, 2019

This paper provides an introduction and a comparison of two widely used evolutionary computation algorithms: Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) based on the previous studies and researches. It describes Genetic Algorithm basic functionalities including various steps such as selection, crossover, and mutation.

2016

Evolutionary algorithms have gained much attention of the researchers as an effective methods for solving different optimization problems. The Genetic Algorithm (GA) is very popular in various fields mainly because of its sense, implementation, and the ability to solve complex problems usually found in engineering systems. The drawback of the GA is that it has high implementation cost and usually requires a higher number of iterations. Particle Swarm Optimization (PSO) is a relatively recent heuristic algorithm which is based on the behavior of swarming characteristics of living organisms. PSO is quite similar to the GA as these two are evolutionary search methods which means that PSO and the GA change from a set of points to another set of points within an iteration with visible improvement from the previous values using some probabilistic and deterministic rules. This paper is used to study the implementation, features and effectiveness of these two evolutionary algorithms.

International Journal of Engineering and Technology

In real world applications, optimization is an inevitable stage in any engineering design. In recent days the optimization theory is also fused into other sciences which require precision in its final result. This topic sounds like a promising domain for research almost in all areas of science and technology. Perhaps several solution methods are proposed for solving problems that require optimization algorithms, in that also the algorithms inspired by natural selection are dominant among them. This paper proposes a hybrid algorithm that integrates two well established methods, one the genetic algorithm (GA) and the other the particle swarm optimization (PSO) algorithm. Here the GA will be the main optimizer and the PSO will be used to guide the GA to locate optimal solutions quickly and effectively. Several benchmark test problems are solved and the applicability of the proposed hybrid algorithm is well established. Keyword-Genetic Algorithm, Particle Swarm Optimization, Soft Computing, Benchmark Problems I. INTRODUCTION In recent days, real world optimization problems any engineering design is a real challenge. Similarly, the optimization theory is also under pining into other sciences which requires the application of cost based design. This topic emerges as a promising topic for research almost in almost diverse fields of science and technology. As said earlier, several solution methods are developed for solving optimization problems, among them the algorithms inspired by natural selection are dominant [1]. In this introduction section, a brief review of literature will be reviewed basically with an intention only on hybrid algorithms that integrates two well established methods, one the genetic algorithm (GA) and the other the particle swarm optimization (PSO) algorithm. In [1], a new evolutionary learning algorithm is based on a hybrid of genetic algorithm (GA) and particle swarm optimization (PSO) called HGAPSO is proposed. Here individuals in a new generation are created, not only by crossover and mutation operation as in GA, but also by PSO. In another research is [2], a hybrid method combining genetic algorithms (GA) and particle swarm optimization (PSO), for the global optimization of multimodal functions is developed. [3] proposes a solution improvement phase can be assisted by knowledge stored within the parent solutions, effectively allowing parents to teach their offspring how to improve their fitness. In this paper, the evolution of each individual of the total population, which consists of the parents and the offspring, is realized with the use of a Particle Swarm Optimizer. Subsequently, a supply chain management with bi-level linear programming to supply chain distribution problem was solved using hybrid of genetic algorithm (GA) and particle swarm optimization (PSO) [4]. In another work, [5], a hybrid Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) method is used for gene selection, and Support Vector Machine (SVM) is adopted as the classifier. In [6], a novel PSO-GA-based hybrid algorithm with "dying probability" for the individuals and the "war/disease process" for the population. [7] introduces, Hybrid Particle Swarm Optimization and Genetic Algorithm (HPSOGA) is proposed to solve the multi-UAV formation reconfiguration problem, which is modeled as a parameter optimization problem. In [8] an evolutionary-based clustering algorithm based on a hybrid of genetic algorithm (GA) and particle swarm optimization algorithm (PSOA) for order clustering in order to reduce surface mount technology (SMT) setup time. Also in [9], novel combined genetic algorithm (GA)/particle swarm optimization (PSO) is presented for optimal location and sizing of DG on distribution systems. Subsequently in [10], Adaptive Genetic Algorithm (AGA) and Particle Swarm Optimization (PSO) are implemented to get optimal schedules and storage assignments for Flexible Manufacturing Systems. Based on the above review of literature confined only to the hybridization of genetic algorithm (GA) and the particle swarm optimization (PSO) algorithm, the functional integration proposed in this research is unique of its kind. It is demonstrated that the extensive search behaviour of GA and the exhaustive search capability of PSO are superior in searching better optimal points. In the following section we will discuss in detail the proposed functional fusion of GA and PSO.

Open Science Framework (OSF) Preprints, 2022

Local optimization with convex function is solved perfectly by traditional mathematical methods such as Newton-Raphson and gradient descent but it is not easy to solve the global optimization with arbitrary function although there are some purely mathematical approaches such as approximation, cutting plane, branch and bound, and interval method which can be impractical because of their complexity and high computation cost. Recently, some evolutional algorithms which are inspired from biological activities are proposed to solve the global optimization by acceptable heuristic level. Among them is particle swarm optimization (PSO) algorithm which is proved as an effective and feasible solution for global optimization in real applications. Although the ideology of PSO is not complicated, it derives many variants, which can make new researchers confused. Therefore, this tutorial focuses on describing, systemizing, and classifying PSO by succinct and straightforward way. Moreover, combinations of PSO and other evolutional algorithms for improving PSO itself or solving other advanced problems are mentioned too.

Journal of Computational and Applied Mathematics, 2011

Heuristic optimization provides a robust and efficient approach for solving complex realworld problems. The aim of this paper is to introduce a hybrid approach combining two heuristic optimization techniques, particle swarm optimization (PSO) and genetic algorithms (GA). Our approach integrates the merits of both GA and PSO and it has two characteristic features. Firstly, the algorithm is initialized by a set of random particles which travel through the search space. During this travel an evolution of these particles is performed by integrating PSO and GA. Secondly, to restrict velocity of the particles and control it, we introduce a modified constriction factor. Finally, the results of various experimental studies using a suite of multimodal test functions taken from the literature have demonstrated the superiority of the proposed approach to finding the global optimal solution.

PLOS ONE

Particle swarm optimization and genetic algorithms are two classes of popular heuristic algorithms that are frequently used for solving complex multi-dimensional mathematical optimization problems, each one with its one advantages and shortcomings. Particle swarm optimization is known to favor exploitation over exploration, and as a result it often converges rapidly to local optima other than the global optimum. The genetic algorithm has the ability to overcome local extrema throughout the optimization process, but it often suffers from slow convergence rates. This paper proposes a new hybrid algorithm that nests particle swarm optimization operations in the genetic algorithm, providing the general population with the exploitation prowess of the genetic algorithm and a sub-population with the high exploitation capabilities of particle swarm optimization. The effectiveness of the proposed algorithm is demonstrated through solutions of several continuous optimization problems, as well...

The most and major popular technique in evolutionary computation research has been the genetic algorithm. Mostly in the Genetic Algorithm(GA), the representation used is a fixed-length bit string. Each position in the string represents a particular feature of an individual and the particular value stored in that corresponding position. Particle Swarm Optimization(PSO) is used to find the optimal fitness value.Simulations are performed over the various standard test data and comparisons are performed with Genetic Algorithm(GA). The experimental results show that proposed PSO based method performs better than the GA method.

Natural computing, 2002

Applied Mathematics and Computation, 2011

Metaheuristic optimization algorithms have become popular choice for solving complex and intricate problems which are otherwise difficult to solve by traditional methods. In the present study an attempt is made to review the hybrid optimization techniques in which one main algorithm is a well known metaheuristic; particle swarm optimization or PSO. Hybridization is a method of combining two (or more) techniques in a judicious manner such that the resulting algorithm contains the positive features of both (or all) the algorithms. Depending on the algorithm/s used we made three classifications as (i) Hybridization of PSO and genetic algorithms (ii) Hybridization of PSO with differential evolution and (iii) Hybridization of PSO with other techniques. Where, other techniques include various local and global search methods. Besides giving the review we also show a comparison of three hybrid PSO algorithms; hybrid differential evolution particle swarm optimization (DE-PSO), adaptive mutation particle swarm optimization (AMPSO) and hybrid genetic algorithm particle swarm optimization (GA-PSO) on a test suite of nine conventional benchmark problems.

Particle swarm optimization (PSO) technique is used mainly for non linear functions. The different types of PSO method are described in this paper. Implementations of different PSO methods are discussed and compared. Relationships between particle swarm optimization and both artificial life and evolutionary computation are reviewed. This paper describes the engineering and computer science aspects of applications, and resources related to particle swarm optimization. PSO is originated in 1995.This paper presents the review of development of PSO as well as recent development in the PSO.