Showing papers on "Firefly algorithm published in 2008"

Nature-Inspired Metaheuristic Algorithms

Abstract: Modern metaheuristic algorithms such as bee algorithms and harmony search start to demonstrate their power in dealing with tough optimization problems and even NP-hard problems. This book reviews and introduces the state-of-the-art nature-inspired metaheuristic algorithms in optimization, including genetic algorithms, bee algorithms, particle swarm optimization, simulated annealing, ant colony optimization, harmony search, and firefly algorithms. We also briefly introduce the photosynthetic algorithm, the enzyme algorithm, and Tabu search. Worked examples with implementation have been used to show how each algorithm works. This book is thus an ideal textbook for an undergraduate and/or graduate course. As some of the algorithms such as the harmony search and firefly algorithms are at the forefront of current research, this book can also serve as a reference book for researchers.

A Modified Particle Swarm Optimization Algorithm

Abstract: Particle swarm optimization algorithm is a kind of auto-adapted search optimization based on community.But the standard particle swarm optimization has the defects of prematurely, stagnation when applied in optimizing problems and easily leading to local minimum. A modified particle swarm optimization algorithm is proposed to improve from the initial solution and the search precision. The results show the algorithm computation precision is increased, the algorithm convergence is improved, and the local minimum phenomenon is mainly avoided. The experimental results of classic functions show that the modified PSO is efficient and feasible.

Hybrid differential evolution - Particle Swarm Optimization algorithm for solving global optimization problems

Abstract: This paper presents a simple, hybrid two phase global optimization algorithm called DE-PSO for solving global optimization problems. DE-PSO consists of alternating phases of differential evolution (DE) and Particle Swarm Optimization (PSO). The algorithm is designed so as to preserve the strengths of both the algorithms. Empirical results show that the proposed DE-PSO is quite competent for solving the considered test functions as well as real life problems.

Improved Particle Swarm Optimization algorithms for electromagnetic optimization

Abstract: Particle Swarm is a relatively novel approach for global stochastic optimization. In this paper some variations over the basic algorithm are proposed, with the aim of a more efficient search over the solution space obtained with a negligible overhead in both complexity and speed. The presented algorithms are then applied to a mathematical test function and to a microwave microstrip filter to show their superior capabilities with respect to the conventional version.

A GA and Particle Swarm Optimization based hybrid algorithm

Abstract: In this paper an improved particle swarm algorithm is presented firstly and then a hybrid method combining genetic algorithm(GA) and particle swarm optimization(PSO) is proposed. This hybrid technique incorporates concepts from GA and PSO and creates individuals in a new generation not only by crossover and mutation operations as found in GA but also by mechanisms of PSO. It can solve the problem of local minimum of the particle swarm optimization and has higher efficiency of search. Simulation results show that the proposed method is effective for the optimization problems.

Initialization and Displacement of the Particles in TRIBES, a Parameter-Free Particle Swarm Optimization Algorithm

Abstract: This chapter presents two ways of improvement for TRIBES, a parameter-free Particle Swarm Optimization (PSO) algorithm. PSO requires the tuning of a set of parameters, and the performance of the algorithm is strongly linked to the values given to the parameter set. However, finding the optimal set of parameters is a very hard and time consuming problem. So, Clerc worked out TRIBES, a totally adaptive algorithm that avoids parameter fitting. Experimental results are encouraging but are still worse than many algorithms. The purpose of this chapter is to demonstrate how TRIBES can be improved by choosing a new way of initialization of the particles and by hybridizing it with an Estimation of Distribution Algorithm (EDA). These two improvements aim at allowing the algorithm to explore as widely as possible the search space and avoid a premature convergence in a local optimum. Obtained results show that, compared to other algorithms, the proposed algorithm gives results either equal or better.

Swarm Intelligence Optimization Algorithm

Abstract: By the use of groups'advantages,in the absence of centralized control and without providing the overall model situation,swarm intelligence optimum algorithm provides the foundation on finding complex distributed solutions to the problem.Introduces the two swarm intelligence algorithm models:ant colony algorithm model and the particle swarm algorithm model,researches on principle mechanism,the basic model,process realization and improved ideas and methods;and by comparing the calculation results of the ant colony algorithm and other heuristic algorithm through the simulation,proved that the ant colony algorithm has a strong ability to find better solutions,and is not easy for a local optimum.The algorithm which based on the population of reservations,parallel global search strategy,using simply speed-displacement model operation,has been made in a higher success rate in practical application.

GAPSO:Effective Genetic Particle Swarm Algorithm and Its Application

Abstract: Based on the genetic algorithm and swam optimization algorithm, a new algorithm (GAPSO) was proposed. This new algorithm mimics the mature process in nature. Optimal individuals of every generation in genetic algorithm get the further improvement by PSO algorithm. Optimization effect of this new algorithm is better than the genetic algorithm and swam optimization algorithm. Application in FCENN design indicates that this new algorithm is indeed better than the genetic algorithm and swam optimization algorithm.

A Hybrid Clustering Algorithm Based on Multi-swarm Constriction PSO and GRASP

Abstract: This paper presents a new hybrid algorithm, which is based on the concepts of Particle Swarm Optimization (PSO) and Greedy Randomized Adaptive Search Procedure (GRASP), for optimally clustering Nobjects into Kclusters. The proposed algorithm is a two phase algorithm which combines a Multi-Swarm Constriction Particle Swarm Optimization algorithm for the solution of the feature selection problem and a GRASP algorithm for the solution of the clustering problem. In this paper in PSO, multiple swarms are used in order to give to the algorithm more exploration and exploitation abilities as the different swarms have the possibility to explore different parts of the solution space and, also, a constriction factor is used for controlling the behaviour of particles in each swarm. The performance of the algorithm is compared with other popular metaheuristic methods like classic genetic algorithms, tabu search, GRASP, ant colony optimization and particle swarm optimization. In order to assess the efficacy of the proposed algorithm, this methodology is evaluated on datasets from the UCI Machine Learning Repository. The high performance of the proposed algorithm is achieved as the algorithm gives very good results and in some instances the percentage of the corrected clustered samples is very high and is larger than 98%.

ESCA: A New Evolutionary-Swarm Cooperative Algorithm

Abstract: A new hybrid approach called Evolutionary Swarm Cooperative Algorithm (ESCA) based on the collaboration between a particle swarm optimization algorithm and an evolutionary algorithm is proposed. ESCA is designed to track moving optima in dynamic environments. ESCA uses three populations of individuals: two EA populations and one particle swarm population. The EA populations evolve by the rules of an evolutionary multimodal optimization algorithm and are used to maintain the diversity of the search. The particle swarm confers precision to the search process. Using the moving peaks benchmark the efficiency of ESCA is evaluated by means of numerical experiments.

Overview of particle swarm optimization algorithm on reactive optimization for power system

Abstract: Particle swarm optimization(PSO)algorithm is a heuristic global optimization technology based on swarm intelligence. Firstly,the principle and process of particle swarm optimization(PSO)algorithm are introduced by this paper.Some kind of improved versions ofPSO and research situation are also presented.Secondly,the characteristic ofthe reactive power optimization in power system and the application of PSO on reactive optimization are given.Finally,the applied prospect of PSO on reactive optimization is pointed out since the reactive power optimization in power system is a typical combination optimization problem with multi-objective,multi-constrained and non-linear.

Improved hybrid particle swarm optimization algorithm

Abstract: The particle swarm optimization algorithm which has slow convergence rate and was easily trapping in local optimum was improved.By changing the velocity updating formula of PSO and by adding the disturbance term,crossover and mutation operator to the algorithm,the hybrid PSO's performance was significant improved.Experimental results indicate that the modified PSO algorithm is effective and has good ability on both global and local optimization problems.

A New Optimization Algorithm for Weight Optimization

Abstract: Particle Swarm Optimization algorithm was developed under the inspiration of behavior laws of bird flocks, fish schools and human communities. Aiming at the disadvantages of Particle Swarm Optimization algorithm like being trapped easily into a local optimum, this paper improves the standard PSO and proposes a new algorithm to solve the overcomes of the standard PSO. The new algorithm keeps not only the fast convergence speed characteristic of PSO, but effectively improves the capability of overall searching as well. We use the new algorithm for the weight optimization in college student evaluation, and compared with PSO, the results show that the new algorithm is efficient.

A Hybrid Algorithm of Simulated Annealing and Particle Swarm Optimization

Abstract: In view of the defects of particle swarm optimization(PSO) algorithm such as easy to get into local extremum,slow convergence in the end of evolution stage and low computational precision,this paper introduces a simulated annealing algorithm(SA) into the PSO algorithm,and proposes a new hybrid optimization algorithm.The hybrid optimization algorithm makes PSO and SA search in various temperatures alternately.It is a kind of double-deck serial structure.PSO provides parallel search structure so that SA can be converted to parallel SA algorithm.Since SA's probability ensures population's diversity,it prevents PSO to get into local extremum.The proposed algorithm keeps the feature of easily implementing original particle swarm optimization,improves the ability of searching the global excellent result,and has fast convergence and high computational precision.The computational result of several benchmark problems shows that the proposed algorithm is superior to the original particle swarm optimization.

Particle Swarm Optimization (PSO) techniques solving Economic Load Dispatch (ELD) Problem

Abstract: This paper presents a Particle Swarm Optimization (PSO) algorithm for Economic Load Dispatch (ELD) problem. The PSO, which is based on the analogy of swarms of birds, is a very robust and efficient algorithm. A swarm of particles are initiated and fly through the solution space, to find the optimal solution. The PSO algorithm belongs to Metaheuristic Optimization techniques. To show its efficiency and effectiveness, the proposed Particle Swarm Optimization (PSO) algorithm is applied to sample ELD problems composed of 4 generators.

The Culture-Based Particle Swarm Optimization Algorithm

Abstract: The particle swarm optimization algorithm based on the intelligent optimization algorithm. But the algorithm easily plunging into the local optimization. For this problem, a new culture-based particle swarm optimization algorithm is proposed in this paper. It constitute with the population space and the belief space. Each space has their own algorithm. Meanwhile, the two spaces communicate with each other by any communication agreement. Both CSPSO and PSO are used to resolve the optimization problems of several widely used test functions, and the results show that CBPSO enhances the global searching ability and has better optimization performance than PSO.

Study on particle swarm algorithm for Job Shop scheduling problems

Abstract: A hybrid particle swarm algorithm is proposed,which is used to make up for the deficiencies of resolving Job Shop scheduling problem and improve the quality of searching solutionsIn the hybrid particle swarm algorithm,the particle swarm algorithm is applied to search in the global solution spaceAccording to the characteristics of job shop solutions,a sort of selection method is proposed based on critical operation,and the taboo search algorithm based on the method is utilized as the local algorithm,thus strengthening the capability of the local searchThe hybrid particle swarm algorithm is tested with 13 hard benchmark problemsThe result shows that the obtained best solution and the average value of ten times result are better than the parallel genetic algorithm and particle swarm algorithmSo it can be concluded that the proposed hybrid particle swarm algorithm is effective

An extended particle swarm optimization algorithm based on coarse-grained and fine-grained criteria and its application

Abstract: In order to study the problem that particle swarm optimization (PSO) algorithm can easily trap into local mechanism when analyzing the high dimensional complex optimization problems, the optimization calculation using the information in the iterative process of more particles was analyzed and the optimal system of particle swarm algorithm was improved. The extended particle swarm optimization algorithm (EPSO) was proposed. The coarse-grained and fine-grained criteria that can control the selection were given to ensure the convergence of the algorithm. The two criteria considered the parameter selection mechanism under the situation of random probability. By adopting MATLAB7.1, the extended particle swarm optimization algorithm was demonstrated in the resource leveling of power project scheduling. EPSO was compared with genetic algorithm (GA) and common PSO, the result indicates that the variance of the objective function of resource leveling is decreased by 7.9%, 18.2%, respectively, certifying the effectiveness and stronger global convergence ability of the EPSO.

A Hybridization of Adaptive Genetic Algorithm and Particle Swarm Optimization for Numerical Optimization Functions

Abstract: Heuristic optimization using hybrid algorithms have provided a robust and efficient approach for solving many optimization problems. In this paper, a new hybrid algorithm using adaptive genetic algorithm (aGA) and particle swarm optimization (PSO) is proposed. The proposed hybrid algorithm is applied to solve numerical optimization functions. The results are compared with those of GA and other conventional PSOs. Finally, the proposed hybrid algorithm outperforms others.

Discrete Particle Swarm Optimization Algorithm for Solving Independent Task Scheduling

Abstract: An improved discrete Particle Swarm Optimization(PSO) algorithm is presented to tackle the independent task scheduling problem. In the algorithm, a task based representation is designed, and a new method is used to update the positions and velocity of particles. In order to keep the particle swarm algorithm from premature stagnation, the simulated annealing algorithm, which has local search ability, is combined with the PSO algorithm to make elaborate search near the optimal solution, then the quality of solutions is improved effectively. Experimental results compared with genetic algorithm and basic PSO algorithm show that the hybrid algorithm has good performance.

Study on multi-objective particle swarm optimization algorithm

Abstract: Over the past decade or so,particle swarm optimization algorithm made great progress in the applied research of multi-objective optimization.Firstly,the basic flow of multi-objective particle swarm optimization(MOPSO) was described,and then the progress of MOPSO algorithm in areas such as algorithm design and application was reviewed,lastly,this analysis and outlook about future research of this algorithm were carried out.

Application of Improved Particle Swarm Optimization in VRP

Abstract: Construct tabu search particle swarm algorithm by merging particle swarm algorithm with tabu search algorithm,which improves the refresh way of the place in PSO.Use it to solve the vehicle routing problem.Based on the implementation of hybrid optimal algorithm and by comparison with the result generated by basic particle swarm algorithm,particle swarm algorithm embedded tabu search algorithm has enhanced the speed of solving evidently and the deficiency of converging slowly.The experiments prove that the hybrid algorithm has better performance and robust.

The Study of PSO with GA

Abstract: Since the 1980s,intelligent optimization algorithms,such as Genetic Algorithm(GA) and Particle Swarm Optimization(PSO) algorithm,have developed by modeling and exploiting some natural phenomenon and processesThey have provided a new dimension for the optimization theoryIt has become a research hot spot of optimization to unit different optimization methods in order to achieve more promising optimization effectsIn this thesis,an improved Hybrid GA-PSO Algorithm is proposed based on PSO(particle swarm algorithm) by using defines a new multi-population scheme and reconstructs the mutation operatorFurther,the recombination operator is also improvedA typical test functions are optimized and the optimization results are analysed

An Effective Hybrid Optimization Algorithm Based on Self-Adaptive Particle Swarm Optimization Algorithm and Artificial Immune Clone Algorithm

Abstract: To balance the exploration and exploitation abilities of particle swarm optimization (PSO), self-adaptive inertia weight factor is introduced in PSO. To improve the ability of each algorithm to escape from a local optimum, a hybrid optimization algorithm (PAHA) based on self-adaptive PSO and artificial immune clone algorithm (AICA) is developed. Simulation results have shown that PAHA is effective and efficient for the optimization problems.

The stop and go particle swarm: A swarm with a dynamically adapting population size

Abstract: In this study, we propose the stop and go particle swarm optimization (PSO) algorithm, a new method to dynamically adapt the PSO population size. Stop and go PSO (SG-PSO) takes advantage of the fact that in practical problems there is a limit to the required accuracy of the optimization result. In SG-PSO, particles are stopped when they have approximately reached the required accuracy. Stopped particles do not consume valuable function evaluations. Still, the information contained in the stopped particles' state is not lost, but rather as the swarm evolves, the particles may become active again, behaving as a memory for the swarm. In addition, as an extension to the SG-PSO algorithm we propose the mixed SG-PSO (MSG-PSO) algorithm. In the MSG-PSO algorithm each particle is given a required accuracy, and through the accuracy settings global search and local search can be balanced. Both SG-PSO and MSG-PSO algorithms are straightforward modifications to the standard PSO algorithm. The SG-PSO algorithm shows strong improvements over the standard PSO algorithm on multimodal benchmark functions from the PSO literature while approximately equivalent results are observed on unimodal benchmark functions. The MSG-PSO algorithm outperforms the standard PSO algorithm on both unimodal and multimodal benchmark functions.

Overview of Particle Swarm Optimization

Abstract: Particle swarm optimization(PSO) algorithm is a new stochastic optimization technique based on swarm intelligenceIt has the advantages of simple concept and easy realization,So it is especially popular in engineering application and research fieldsThis paper intro-duces and analyzes systematically the principle,process and various improved algorithmsAt last,applications of PSO algorithm are summed up,and further research issues and some suggestions are given

Reactive Power Optimization Based on Particle Swarm Optimization Algorithm With Immunity

Abstract: To improve the convergence performance of particle swarm optimization(PSO) algorithm,the immunological memory and self-regulation mechanism are led into standard PSO to form particle swarm optimization algorithm with immunity. In the proposed algorithm the diversity holding strategy based on particle concentration mechanism is adopted,meanwhile the vaccination and immunoselection in immune algorithm are used to guide the search process. The improved PSO can hold the diversity of swarm during the optimization process,and eliminate the degeneration phenomenon occurring in the optimization process,thus both convergence accuracy and convergence speed of the improved algorithm can be ensured. The simulation of IEEE 30-bus system that is taken as case study is simulated and the simulation results show that comparing with standard PSO algorithm,the proposed PSO algorithm with immunity possesses following advantages such as high convergence speed,accurate and the ability of getting rid of local optima in time to achieve global optimal solution,etc.

A Multiagent Genetic Particle Swarm Optimization

Abstract: The efforts of this paper are proposing a multi-agent genetic particle swarm optimization algorithm (MAGPSO) by introducing the multi-agent system to the particle swarm optimization(PSO) algorithm. Through the competition and cooperation operation with its neighbors, the neighborhood random crossing operation within its neighboring area, the mutation operation, and combining the evolutionary mechanism of the PSO algorithm, every individual senses local environment unceasingly, and affects the entire agent grid gradually, so that it enhances its fitness to the environment. This algorithm can maintain the diversity of the swarm effectively, and improve the precision of optimization, and simultaneously, restrain the prematurity phenomenon efficiently. The results of testing three high dimension benchmark function and comparing with some optimization results of other methods illustrate this algorithm has higher optimization performance in the field of high dimension functions optimization.