Particle swarm and ant colony algorithms hybridized for improved continuous optimization

Shelokar, Prakash; Siarry, Patrick; Jayaraman, Vaidyanathan; Kulkarni, Bhaskar D.

doi:10.1016/j.amc.2006.09.098

Cited by 282 publications

(160 citation statements)

References 15 publications

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“…PSO was originally used to solve non-linear continuous optimization problems, but more recently it has been used in many practical, real-life application problems [21]. PSO proved to be a successful approach to solve complex continuous problems and is proved to be efficient and robust for solution of combinatorial optimization problems [22].…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

A particle swarm optimization algorithm for the continuous absolute p-center location problem with Euclidean distance

Rabie¹,

El-Khodary²,

Tharwat³

2013

IJACSA

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Abstract-The p-center location problem is concerned with determining the location of p centers in a plane/space to serve n demand points having fixed locations. The continuous absolute pcenter location problem attempts to locate facilities anywhere in a space/plane with Euclidean distance. The continuous Euclidean p-center location problem seeks to locate p facilities so that the maximum Euclidean distance to a set of n demand points is minimized. A particle swarm optimization (PSO) algorithm previously advised for the solution of the absolute p-center problem on a network has been extended to solve the absolute pcenter problem on space/plan with Euclidean distance. In this paper we develop a PSO algorithm for the continuous absolute pcenter location problem to minimize the maximum Euclidean distance from each customer to his/her nearest facility, called "PSO-ED". This problem is proven to be NP-hard. We tested the proposed algorithm "PSO-ED" on a set of 2D and 3D problems and compared the results with a branch and bound algorithm. The numerical experiments show that PSO-ED algorithm can solve optimally location problems with Euclidean distance including up to 1,904,711 points.

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Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

A particle swarm optimization algorithm for the continuous absolute p-center location problem with Euclidean distance

Rabie¹,

El-Khodary²,

Tharwat³

2013

IJACSA

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“…Meanwhile, it is impossible to obtain the data by only once in the linear optimization application. Furthermore, only these nearest data could perform importance for the optimization [3][4]. In order to resolve the online recursive optimization, the recursive particle swarm optimization (R-PSO) is proposed in this paper.…”

Section: Introductionmentioning

confidence: 99%

The Vector Clustering Based on the Recursive Particle Swarm Optimization with Radial Basis Function Networks Modeling System

Jia¹

2017

dteees

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Abstract. The vector clustering plays an important role to the applications of information science, such as information compression. The particle swarm optimization is used to implement the clustering operation. In this paper, a Recursive Particle Swarm Optimization (RPSO) is proposed to solve dynamic optimization problems where the data is obtained not once but one by one. The position of each particle swarm is updated recursively based on the continuous data and the historical knowledge. The experiment results indicate that RPSO-based radial basis function networks needs fewer radial basis functions and gives more accurate results than traditional PSO in solving dynamic problems. Then the proposed RPSO is suggested to cluster statistic counting vectors in order to enhance the performance of the data compression.

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“…Inspired by the phenomenon of symbiosis in natural ecosystem, Niu et al [24] incorporate master-slave mode into PSO and presents a multi-population cooperative particle swarm optimization (MCPSO). Shelokar et al [25] propose an improved particle swarm optimization hybridized with an ant colony approach, called PSACO (particle swarm ant colony optimization), for optimization of multi-modal continuous functions. The proposed method applies PSO for global optimization and the idea of ant colony approach to update positions of particles to attain rapidly the feasible solution space.…”

Section: Introductionmentioning

confidence: 99%

Improved particle swarm algorithm for hydrological parameter optimization

Jiang

Liu

Huang

et al. 2010

Applied Mathematics and Computation

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a b s t r a c tIn this paper, a new method named MSSE-PSO (master-slave swarms shuffling evolution algorithm based on particle swarm optimization) is proposed. Firstly, a population of points is sampled randomly from the feasible space, and then partitioned into several sub-swarms (one master swarm and other slave swarms). Each slave swarm independently executes PSO or its variants, including the update of particles' position and velocity. For the master swarm, the particles enhance themselves based on the social knowledge of master swarm and that of slave swarms. At periodic stage in the evolution, the master swarm and the whole slave swarms are forced to mix, and points are then reassigned to several subswarms to ensure the share of information. The process is repeated until a user-defined stopping criterion is reached. The tests of numerical simulation and the case study on hydrological model show that MSSE-PSO remarkably improves the accuracy of calibration, reduces the time of computation and enhances the performance of stability. Therefore, it is an effective and efficient global optimization method.

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Particle swarm and ant colony algorithms hybridized for improved continuous optimization

Cited by 282 publications

References 15 publications

A particle swarm optimization algorithm for the continuous absolute p-center location problem with Euclidean distance

A particle swarm optimization algorithm for the continuous absolute p-center location problem with Euclidean distance

The Vector Clustering Based on the Recursive Particle Swarm Optimization with Radial Basis Function Networks Modeling System

Improved particle swarm algorithm for hydrological parameter optimization

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