Showing papers on "Firefly algorithm published in 2009"

Firefly algorithms for multimodal optimization

Abstract: Nature-inspired algorithms are among the most powerful algorithms for optimization. This paper intends to provide a detailed description of a new Firefly Algorithm (FA) for multimodal optimization applications. We will compare the proposed firefly algorithm with other metaheuristic algorithms such as particle swarm optimization (PSO). Simulations and results indicate that the proposed firefly algorithm is superior to existing metaheuristic algorithms. Finally we will discuss its applications and implications for further research.

Firefly Algorithm for Continuous Constrained Optimization Tasks

Abstract: The paper provides an insight into the improved novel metaheuristics of the Firefly Algorithm for constrained continuous optimization tasks. The presented technique is inspired by social behavior of fireflies and the phenomenon of bioluminescent communication. The first part of the paper is devoted to the detailed description of the existing algorithm. Then some suggestions for extending the simple scheme of the technique under consideration are presented. Subsequent sections concentrate on the performed experimental parameter studies and a comparison with existing Particle Swarm Optimization strategy based on existing benchmark instances. Finally some concluding remarks on possible algorithm extensions are given, as well as some properties of the presented approach and comments on its performance in the constrained continuous optimization tasks.

Particle Swarm Optimization Algorithm

Abstract: Particle swarm optimization (PSO) with the typical characteristic of swarm intelligence is a kind of novel evolution algorithm after ant colony algorithm. First,the basic principles of PSO and improved algorithm were introduced. Then,the research progress on PSO algorithm was summarized in such aspects as organization and evolution of population, hybrid PSO algorithm etc.

Application of particle swarm optimization algorithm for solving bi-level linear programming problem

Abstract: Bi-level linear programming is a technique for modeling decentralized decision. It consists of the upper-level and lower-level objectives. This paper attempts to develop an efficient method based on particle swarm optimization (PSO) algorithm with swarm intelligence. The performance of the proposed method is ascertained by comparing the results with genetic algorithm (GA) using four problems in the literature and an example of supply chain model. The results illustrate that the PSO algorithm outperforms GA in accuracy.

Survey of particle swarm optimization algorithm

Abstract: In order to promote the applications of particle swarm optimization algorithm(PSO),and provides the relevant information for the further research on this algorithm,a review on the recent progress of PSO is given.Based on the introduces of PSO's basic principles and mechanism,a thorough investigation on the research progress of PSO is given in aspects of parameter setting,convergence characteristic,topology,hybrid algorithm,and the applications in continuous and discrete domains.Finally,the future research issues of the PSO are given.

Firefly clock synchronization in an 802.15.4 wireless network

Abstract: This paper describes the design and implementation of a distributed self-stabilizing clock synchronization algorithm based on the biological example of Asian Fireflies. Huge swarms of these fireflies use the principle of pulse coupled oscillators in order to synchronously emit light flashes to attract mating partners. When applying this algorithm to real sensor networks, typically, nodes cannot receive messages while transmitting, which prevents the networked nodes from reaching synchronization. In order to counteract this deafness problem, we adopt a variant of the Reachback Firefly Algorithm to distribute the timing of light flashes in a given time window without affecting the quality of the synchronization. A case study implemented on 802.15.4 Zigbee nodes presents the application of this approach for a time-triggered communication scheduling and coordinated duty cycling in order to enhance the battery lifetime of the nodes.

An improved particle swarm optimization algorithm for optimal power flow

Abstract: This paper presents the solution of optimal power flow using particle swarm optimization algorithm. This paper proposes a novel improved particle swarm optimization for solving the optimal power flow problem. This method can be divided into two parts. In the first part a multi-start technique is introduced to overcome premature convergence, while the other part employs improved particle swarm optimization algorithm to obtain the optimal solution. IEEE 30-bus system is used to test the performance of this solution technique, and the numerical results show that the proposed algorithm is superior to genetic algorithm and conventional particle swarm optimization algorithm for the optimal power flow problem.

A particle swarm optimization algorithm on job-shop scheduling problems with multi-purpose machines

Abstract: This paper is a contribution to the research which aims to provide an efficient optimization algorithm for job-shop scheduling problems with multi-purpose machines or MPMJSP. To meet its objective, this paper proposes a new variant of particle swarm optimization algorithm, called GLN-PSOc, which is an extension of the standard particle swarm optimization algorithm that uses multiple social learning topologies in its evolutionary process. GLN-PSOc is a metaheuristic that can be applied to many types of optimization problems, where MPMJSP is one of these types. To apply GLN-PSOc in MPMJSP, a procedure to map the position of particle into the solution of MPMJSP is proposed. Throughout this paper, GLN-PSOc combined with this procedure is named MPMJSP-PSO. The performance of MPMJSP-PSO is evaluated on well-known benchmark instances, and the numerical results show that MPMJSP-PSO performs well in terms of solution quality and that new best known solutions were found in some instances of the test problems.

Particle Swarm Optimization Algorithm for Agent-Based Artificial Markets

Abstract: Particle swarm optimization (PSO) is adapted to simulate dynamic economic games. The robustness and speed of the PSO algorithm is compared to a genetic algorithm (GA) in a Cournot oligopsony market. Artificial agents with the PSO learning algorithm find the optimal strategies that are predicted by theory. PSO is simpler and more robust to changes in algorithm parameters than GA. PSO also converges faster and gives more precise answers than the GA method which was used by some previous economic studies.

A new diversity guided particle swarm optimization with mutation

Abstract: This paper presents a new diversity guided Particle Swarm Optimization algorithm (PSO) named Beta Mutation PSO or BMPSO for solving global optimization problems. The BMPSO algorithm makes use of an evolutionary programming based mutation operator to maintain the level of diversity in the swarm population, thereby maintaining a good balance between the exploration and exploitation phenomena and preventing premature convergence. Beta distribution is used to perform the mutation in the proposed BMPSO algorithm. The performance of the BMPSO algorithm is investigated on a set of ten standard benchmark problems and the results are compared with the original PSO algorithm. The numerical results show that the proposed algorithm outperforms the basic PSO algorithm in all the test cases taken in this study.

A dynamic global and local combined particle swarm optimization algorithm

Abstract: Particle swarm optimization (PSO) algorithm has been developing rapidly and many results have been reported. PSO algorithm has shown some important advantages by providing high speed of convergence in specific problems, but it has a tendency to get stuck in a near optimal solution and one may find it difficult to improve solution accuracy by fine tuning. This paper presents a dynamic global and local combined particle swarm optimization (DGLCPSO) algorithm to improve the performance of original PSO, in which all particles dynamically share the best information of the local particle, global particle and group particles. It is tested with a set of eight benchmark functions with different dimensions and compared with original PSO. Experimental results indicate that the DGLCPSO algorithm improves the search performance on the benchmark functions significantly, and shows the effectiveness of the algorithm to solve optimization problems.

A theoretical and empirical analysis of convergence related particle swarm optimization

Abstract: In this paper an extensive theoretical and empirical analysis of recently introduced Particle Swarm Optimization algorithm with Convergence Related parameters (CR-PSO) is presented. The convergence of the classical PSO algorithm is addressed in detail. The conditions that should be imposed on parameters of the algorithm in order for it to converge in mean-square have been derived. The practical implications of these conditions have been discussed. Based on these implications a novel, recently proposed parameterization scheme for the PSO has been introduced. The novel optimizer is tested on an extended set of benchmarks and the results are compared to the PSO with time-varying acceleration coefficients (TVAC-PSO) and the standard genetic algorithm (GA).

Use of the particle swarm optimization algorithm for second order design of levelling networks

Abstract: Abstract The weight problem in geodetic networks can be dealt with as an optimization procedure. This classic problem of geodetic network optimization is also known as second-order design. The basic principles of geodetic network optimization are reviewed. Then the particle swarm optimization (PSO) algorithm is applied to a geodetic levelling network in order to solve the second-order design problem. PSO, which is an iterative-stochastic search algorithm in swarm intelligence, emulates the collective behaviour of bird flocking, fish schooling or bee swarming, to converge probabilistically to the global optimum. Furthermore, it is a powerful method because it is easy to implement and computationally efficient. Second-order design of a geodetic levelling network using PSO yields a practically realizable solution. It is also suitable for non-linear matrix functions that are very often encountered in geodetic network optimization. The fundamentals of the method and a numeric example are given.

A Novel Multi-Swarm Particle Swarm Optimization Algorithm Applied in Active Contour Model

Abstract: PSO (particle swarm optimization) algorithm provides a robust and efficient approach for searching for the object's concavities with the snake model.However, since single particle swarm optimization algorithm converges slowly and easily converges to local optima, it is not suitable well to be applied in active contour model directly. In this paper, a novel multi-swarm particle swarm optimization method was proposed to solve this problem. The proposed algorithm could expand the control point of the searching area and optimize convergence speed. It sets swarm for each control point and then every swarm search best point collaboratively through shared information, so it avoids the premature deficiency in traditional PSO algorithm. Compared our proposed algorithm with traditional algorithm, the experimental results showed that our method has superior performance than conventional snake model without spending extra time.

Novel Mutative Particle Swarm Optimization Algorithm for Discrete Optimization.

Abstract: Mutative Particle Swarm Optimization (MPSO) is swarm-based stochastic optimization algorithm combined with the mutative function inspired by Genetic Algorithm (GA). The algorithm searches for the solution by combining swarming behavior, as well as mutation of the particles to accelerate the search process. This paper presents a modification to the MPSO algorithm for it to solve Integer Programming (IP) problems. The proposed approach was compared with the works of Parsopoulos & Vrahatis on the Seven Integer Programming (SIP) benchmark functions. The results show that the proposed method was able to outperform the performance in Parsopoulos & Vrahatis, with better convergence at a significantly faster rate.

A New Optimizaiton Algorithm for Function Optimization

Abstract: Particle Swarm Optimization (PSO) algorithm was developed under the inspiration of behavior laws of bird flocks, fish schools and human communities. In order to get rid of the disadvantages of standard 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 these problems. The new algorithm keeps not only the fast convergence speed characteristic of PSO, but effectively improves the capability of global searching as well. Compared with standard PSO on the Benchmarks function, the new algorithm produces more efficient results.

Hybrid Genetic Algorithm with Particle Swarm Optimization Technique

Abstract: This paper proposes a new hybrid genetic algorithm which combine with the particle swarm optimization technique in order to improve the search efficiency of classical genetic algorithm. This algorithm gives a new crossover operation and a mutation strategy based on the idea of particle swarm optimization. The experiment results show that the new algorithm can obtain better results than competitive algorithm in the average convergence generation and the global convergence probability.

Firefly Algorithm, Lévy Flights and Global Optimization.

Abstract: Nature-inspired algorithms such as Particle Swarm Optimization and Firefly Algorithm are among the most powerful algorithms for optimization. In this paper, we intend to formulate a new metaheuristic algorithm by combining Levy flights with the search strategy via the Firefly Algorithm. Numerical studies and results suggest that the proposed Levy-flight firefly algorithm is superior to existing metaheuristic algorithms. Finally implications for further research and wider applications will be discussed.

A Modified Particle Swarm Optimization Algorithm

Abstract: A modified particle swarm optimization algorithm is proposed in this paper. In the presented algorithm, every particle chooses its inertial factor according to the approaching degree between the fitness of itself and the optimal particle. Simultaneously, a random number is introduced into the algorithm in order to jump out from local optimum and a minimum factor is used to avoid premature convergence. Five well-known functions are chosen to test the performance of the suggested algorithm and the influence of the parameter on performance. Simulation results show that the modified algorithm has advantage of global convergence property and can effectively alleviate the problem of premature convergence. At the same time, the experimental results also show that the suggested algorithm is greatly superior to PSO and APSO in terms of robustness.

Reachback Firefly Synchronicity with Late Sensitivity Window in Wireless Sensor Networks

Abstract: Event synchronicity is a popular requirement in wireless sensor network. Firefly synchronicity phenomenon provides some inspiration. In this paper, we introduce a late sensitivity window to the Reachback Firefly Algorithm. The simulation result shows that the introduction of late sensitivity window not only reduces the time to synchronization, but also the overload of the network. Furthermore, it releases the dependency on choosing of optimized parameters, such as the couple strength, to work in a specific environment. Thus, synchronization on a network can be easily achieved in various scenarios.

An Improved Particle Swarm Optimization for Traveling Salesman Problem

Abstract: In allusion to particle swarm optimization being prone to get into local minimum, an improved particle swarm optimization algorithm is proposed. The algorithm draws on the thinking of the greedy algorithm to initialize the particle swarm. Two swarms are used to optimize synchronously. Crossover and mutation operators in genetic algorithm are introduced into the new algorithm to realize the sharing of information among swarms. We test the algorithm with Traveling Salesman Problem with 14 nodes and 30 nodes. The result shows that the algorithm can break away from local minimum earlier and it has high convergence speed and convergence ratio.

A novel adaptive particle swarm optimization to solve traveling salesman problem

Abstract: Particle swarm optimization (PSO) is a kind of evolutionary algorithm to find optimal (or near optimal) solutions for numerical and qualitative problems. In this paper, a new variation on the traditional PSO algorithm, called adaptive particle swarm optimization (APSO), has been proposed, employing adaptive behavior to significantly improve the performance of the original algorithm. Every particle chooses its inertial factor according to the fitness of itself and the optimal particle in the presented algorithm. Finally, Traveling salesman problem (TSP) is applied to show the effectiveness of the proposed PSO. Simulation results show that the new algorithm has advantage of global convergence property and can effectively alleviate the problem of premature convergence.

Stochastic Optimization Problem through Particle Swarm Optimization Algorithm

Abstract: In this paper, two classes of stochastic optimization problems, which are expected value models and chance-constrained programming, are introduced. In order to solve the problems, the method of stochastic simulation is used to generate training samples for neural network, and then particle swarm optimization algorithm and neural network are integrated to produce a hybrid intelligent algorithm. Two numerical examples are provided to illustrate the effectiveness of the hybrid particle swarm optimization algorithm.

A Fuzzy Particle Swarm Optimization Algorithm for a Cell Formation Problem.

Abstract: Group technology (GT) is a useful way to increase productivity with high quality in cellular manufacturing systems (CMSs), in which cell formation (CF) is a key step in the GT philosophy. When boundaries between groups are fuzzy, fuzzy clustering has been successfully adapted to solve the CF problem; however, it may result uneven distribution of parts/machines where the problem becomes larger. In this case, particle swarm optimization (PSO) can be used to tackle such a hard problem. This paper proposes a hybrid algorithm based on the fuzzy clustering and particle swarm optimization (FPSO) to solve the given CF problem. We experiment a number of examples to show the efficiency of the proposed algorithm and find that our proposed FPSO algorithm is able to obtain good results at reasonable time. Keywords— Cellular manufacturing systems, cell formation, fuzzy clustering, particle swarm optimization (PSO)

Particle swarm optimization algorithm and its application in neural networks

Abstract: The fundamental particle swarm optimization PSO algorithm is improved and the particle swarm optimization algorithm is combined with error back-propagation BP algorthm.With the traditional BP network diagnosis this method is a novel diagnosis with fast convergence speed and higher convergence precision.

Multi-objective particle swarm optimization algorithm based on game strategies

Abstract: Particle Swarm Optimization (PSO) is easier to realize and has a better performance than evolutionary algorithm in many fields. This paper proposes a novel multi-objective particle swarm optimization algorithm inspired from Game Strategies (GMOPSO), where those optimized objectives are looked as some independent agents which tend to optimize own objective function. Therefore, a multi- player game model is adopted into the multi-objective particle swarm algorithm, where appropriate game strategies could bring better multi-objective optimization performance. In the algorithm, novel bargain strategy among multiple agents and nondominated solutions archive method are designed for improving optimization performance. Moreover, the algorithm is validated by several simulation experiments and its performance is tested by different benchmark functions.

An Adaptive Hybrid Particle Swarm Optimization

Abstract: The particle swarm optimization (PSO) algorithm is vulnerable to reach local optimal value. So, this paper presents an adaptive hybrid particles swarm optimization. During the solving process, both crossover operator in genetic algorithm and hyper-mutation are introduced. Referring to the selection mechanism of immune algorithm based on information entropy, the adaptive selections mechanism is proposed. Experiments show that the algorithm effectively improves global search capability.

A PSO --- Line Search Hybrid Algorithm

Abstract: Recently, Particle Swarm Optimization (PSO), gained vast attention and applied to variety of engineering optimization problems because of its simplicity and efficiency. The performance of the PSO algorithm can be further improved by hybrid techniques. There are numerous hybrid PSO algorithms published in the literature where researchers combine the benefits of PSO with other heuristic algorithms. In this paper, we propose a cooperative line search particle swarm optimization (CLS-PSO) algorithm by integrating local line search technique and standard PSO (S-PSO). The performance of the proposed hybrid algorithm, examined through six typical nonlinear optimization problems, is reported. Our experimental results show that CLS-PSO outperforms S-PSO.