A hierarchical particle swarm optimizer and its adaptive variant

Janson, Stefan; Middendorf, Martin

doi:10.1109/tsmcb.2005.850530

Cited by 308 publications

(153 citation statements)

References 24 publications

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“…Performance of GD-FF algorithm is tested on a number of benchmark functions (table 1) which have been extensively used [19]. The benchmark functions include two unimodal functions, Rosenbrock and Sphere, and three multimodal functions, Rastrigin, Griewank and Ackley.…”

Section: Resultsmentioning

confidence: 99%

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A Gaussian Firefly Algorithm

Farahani¹,

Abshouri²,

Nasiri³

et al. 2011

IJMLC

140

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Abstract-Firefly algorithm is one of the evolutionary optimization algorithms, and is inspired by fireflies behavior in nature. Each firefly movement is based on absorption of the other one. In this paper to stabilize firefly's movement, it is proposed a new behavior to direct fireflies movement to global best if there was no any better solution around them. In addition to increase convergence speed it is proposed to use Gaussian distribution to move all fireflies to global best in each iteration. Proposed algorithm was tested on five standard functions that have ever used for testing the static optimization algorithms. Experimental results show better performance and more accuracy than standard Firefly algorithm.

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Section: Resultsmentioning

confidence: 99%

“…However, the attractiveness β is relative, it should vary with the distance r between firefly i and firefly j. As light intensity decreases with the distance from its source and light is also absorbed in the media, so we should allow the attractiveness to vary with degree of absorption [19], [12].…”

Section: Firefly Algorithmmentioning

confidence: 99%

A Gaussian Firefly Algorithm

Farahani¹,

Abshouri²,

Nasiri³

et al. 2011

IJMLC

140

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“…Performance of the proposed PSO models is tested on a number of benchmark functions (Table 1) which have been extensively used in the literature [40]. The benchmark functions include two unimodal functions, Rosenbrock and Sphere and three multimodal functions, Rastrigin, Griewank, and Ackley.…”

Section: Experimental Studymentioning

confidence: 99%

A note on the learning automata based algorithms for adaptive parameter selection in PSO

Hashemi

Meybodi

2011

Applied Soft Computing

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PSO, like many stochastic search methods, is very sensitive to efficient parameter setting such that modifying a single parameter may cause a considerable change in the result. In this paper, we study the ability of learning automata for adaptive PSO parameter selection. We introduced two classes of learning automata based algorithms for adaptive selection of value for inertia weight and acceleration coefficients. In the first class, particles of a swarm use the same parameter values adjusted by learning automata. In the second class, each particle has its own characteristics and sets its parameter values individually. In addition, for both classed of proposed algorithms, two approaches for changing value of the parameters has been applied. In first approach, named adventurous, value of a parameter is selected from a finite set while in the second approach, named conservative, value of a parameter either changes by a fixed amount or remains unchanged. Experimental results show that proposed learning automata based algorithms compared to other schemes such as SPSO, PSOIW, PSO-TVAC, PSOLP, DAPSO, GPSO, and DCPSO have the same or even higher ability to find better solutions. In addition, proposed algorithms converge to stopping criteria for some of the highly multi modal functions significantly faster.

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“…Concept of discrete particle swarm optimization [2] is used to effectively map job scheduling solution to PSO particle. Hierarchical particle swarm optimization (HPSO) is another such meta-heuristic proposed by Janson and Middendorf [3] as a variant of PSO. It has increased diversity of population and better convergence.…”

Section: Introductionmentioning

confidence: 99%

“…The changing arrangement of individuals helps preserving diversity, avoid premature convergence in the early iterations and promote convergence towards global optimum. Hierarchical particle swarm optimization introduced in [3] has been successfully applied in solving continuous optimization functions. It was also used as an efficient method for economic load dispatch problem [21].…”

Section: Introductionmentioning

confidence: 99%

Enhancing the Discrete Particle Swarm Optimization based Workflow Grid Scheduling using Hierarchical Structure

Garg¹,

Singh²

2013

IJCNIS

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-The problem of scheduling dependent tasks (DAG) is an important version of scheduling, to efficiently exploit the computational capabilities of grid systems. The problem of scheduling tasks of a graph onto a set of different machines is an NP Complete problem. As a result, a number of heuristic and meta-heuristic approaches are used over the years due to their ability of providing high quality solutions with reasonable computation time. Discrete Particle Swarm Optimization is one such meta-heuristic used for solving the discrete problem of grid scheduling, but this method converge to sub optimal solutions due to premature convergence. To deal with premature convergence, in this paper we proposed the design and implementation of hierarchical discrete particle swarm optimization (H-DPSO) for dependent task scheduling in grid environment. In H-DPSO particles are arranged in dynamic hierarchy where good particles lying above in hierarchy are having larger influence on the swarm. We consider the bi-objective version of problem to minimize makespan and total cost simultaneously as the optimization criteria. The H-DPSO based scheduler was evaluated under different application task graphs. Simulation analysis manifests that H-DPSO based scheduling is highly viable and effective approach for grid computing.

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A hierarchical particle swarm optimizer and its adaptive variant

Cited by 308 publications

References 24 publications

A Gaussian Firefly Algorithm

A Gaussian Firefly Algorithm

A note on the learning automata based algorithms for adaptive parameter selection in PSO

Enhancing the Discrete Particle Swarm Optimization based Workflow Grid Scheduling using Hierarchical Structure

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