Swarm Intelligence: Foundations, Perspectives and Applications

Abraham, Ajith; Guo, Haijuan; Liu, Hongbo

doi:10.1007/978-3-540-33869-7_1

Cited by 179 publications

(116 citation statements)

References 22 publications

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“…The role of the inertia weight, w in equation (5), is considered critical for the convergence of PSO [33]. The inertia weight is used to control the impact of the previous velocity of particle on its current velocity.…”

Section: Pso With Time Varying Parametersmentioning

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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Section: Pso With Time Varying Parametersmentioning

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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“…Ant Colony Optimization (ACO) [13,14] is a popular meta-heuristics based on certain behavioral patterns of foraging ants. Ants have shown ability to find optimal paths between their nest and source of food.…”

Section: Ant Colony Optimization For Personalized Searchmentioning

confidence: 99%

“…Emulation of ants' behavior can be used as probabilistic computational technique for solving complex problems which can be reduced to finding optimal paths [13,14]. An artificial ant k placed in vertex i moves to node j with probability p ij k :…”

Section: Ant Colony Optimization For Personalized Searchmentioning

confidence: 99%

Implicit User Modelling Using Hybrid Meta-Heuristics

Krömer

Snášel

Platoš

et al. 2008

2008 Eighth International Conference on Hybrid Intelligent Systems

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“…It could be implemented and applied to solve various function optimization problems, or the problems that can be transformed to function optimization problems. As an algorithm, the main strength of PSO is its fast convergence, which compares favorably with many global optimization algorithms [16]. In this chapter, we explore the neighbor-selection problem based PSO for P2P Networks.…”

Section: Introductionmentioning

confidence: 99%

Neighbor Selection in Peer-to-Peer Overlay Networks: A Swarm Intelligence Approach

Liu

Abraham

Badr

2009

Computer Communications and Networks

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Summary. Peer-to-peer (P2P) topology has a significant influence on the performance, search efficiency and functionality, and scalability of the application. In this chapter, we investigate a multi-swarm approach to the problem of Neighbor Selection (NS) in P2P networks. Particle swarm share some common characteristics with P2P in the dynamic socially environment. Each particle encodes the upper half of the peer-connection matrix through the undirected graph, which reduces the search space dimension. The portion of the adjustment to the velocity influenced by the individual's cognition, the group cognition from multi-swarms, and the social cognition from the whole swarm, makes an important influence on the particles' ergodic and synergetic performance. We also attempt to theoretically prove that the multiswarm optimization algorithm converges with a probability of 1 towards the global optima. The performance of our approach is evaluated and compared with other two different algorithms. The results indicate that it usually required shorter time to obtain better results than the other considered methods, specially for large scale problems.

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Swarm Intelligence: Foundations, Perspectives and Applications

Cited by 179 publications

References 22 publications

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

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

Implicit User Modelling Using Hybrid Meta-Heuristics

Neighbor Selection in Peer-to-Peer Overlay Networks: A Swarm Intelligence Approach

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