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

Hashemi, Ali; Meybodi, M. R.

doi:10.1016/j.asoc.2009.12.030

Cited by 95 publications

(48 citation statements)

References 36 publications

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“…For examining the proposed methods have done experiments on four famous standard functions that usually they are used as criterion of evaluating methods in most of literature [14]. Functions that are used consist of Sphere, Rastrigin, Ackley, and Rosenbrock which have been defined by equations of (3) to (6) respectively [14].…”

Section: Resultsmentioning

confidence: 99%

“…Functions that are used consist of Sphere, Rastrigin, Ackley, and Rosenbrock which have been defined by equations of (3) to (6) respectively [14]. …”

Section: Resultsmentioning

confidence: 99%

“…Each algorithm has modeled part of natural immune system. According to the literature, these algorithms have been used for solving optimization problems, pattern recognition, classification, clustering, intrusion detection, and other computer problems and have obtained good results relative to existing algorithms [14] [16]. Generally, can be consider the AIS as an adaptive, very parallel and distributed system.…”

Section: Artificial Immune Systemmentioning

confidence: 99%

“…The environment returns a reinforcement signal that shows the relative quality of an action of the LA, and then LA adjusts itself by means of a learning algorithm. LA have been used to enhance the learning capability of other algorithms such as neural networks [17], genetic algorithms [32], particle swarm optimization [14] [31] and ant colony optimization [33].…”

Section: Introductionmentioning

confidence: 99%

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LACAIS: Learning Automata Based Cooperative Artificial Immune System for Function Optimization

Rezvanian

Meybodi

2010

Communications in Computer and Information Science

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Abstract. Artificial Immune System (AIS) is taken into account from evolutionary algorithms that have been inspired from defensive mechanism of complex natural immune system. For using this algorithm like other evolutionary algorithms, it should be regulated many parameters, which usually they confront researchers with difficulties. Also another weakness of AIS especially in multimodal problems is trapping in local minima. In basic method, mutation rate changes as only and most important factor results in convergence rate changes and falling in local optima. This paper presented two hybrid algorithm using learning automata to improve the performance of AIS. In the first algorithm entitled LA-AIS has been used one learning automata for tuning the hypermutation rate of AIS and also creating a balance between the process of global and local search. In the second algorithm entitled LA-CAIS has been used two learning automata for cooperative antibodies in the evolution process. Experimental results on several standard functions have shown that the two proposed method are superior to some AIS versions.

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Section: Resultsmentioning

confidence: 99%

“…Functions that are used consist of Sphere, Rastrigin, Ackley, and Rosenbrock which have been defined by equations of (3) to (6) respectively [14]. …”

Section: Resultsmentioning

confidence: 99%

Section: Artificial Immune Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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LACAIS: Learning Automata Based Cooperative Artificial Immune System for Function Optimization

Rezvanian

Meybodi

2010

Communications in Computer and Information Science

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“…Since the first introduction of LA, there have been many applications of this learning method such as traffic congestion [11], channel assignment [12] in cellular mobile networks and dynamic point coverage in wireless sensor networks [13]. More recently, LA has been successfully applied to the context of PSO for adaptive parameter selection [14]. Also in [15] a new hybrid model of PSO and cellular automata is developed to address the dynamic optimization.…”

Section: *Manuscriptmentioning

confidence: 99%

Adaptive cooperative particle swarm optimizer

2013

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A distributed adaptive landmark clustering algorithm based on mOverlay and learning automata for topology mismatch problem in unstructured peer‐to‐peer networks

Saghiri

Meybodi

2015

Int J Communication

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Peer-to-peer networks are overlay networks that are built on top of communication networks that are called underlay networks. In these networks, peers are unaware of the underlying networks, so the peers choose their neighbors without considering the underlay positions, and therefore, the resultant overlay network may have mismatches with its underlying network, causing redundant end-to-end delay. Landmark clustering algorithms, such as mOverlay, are used to solve topology mismatch problem. In the mOverlay algorithm, the overlay network is formed by clusters in which each cluster has a landmark peer. One of the drawbacks of mOverlay is that the selected landmark peer for each cluster is fixed during the operation of the network. Because of the dynamic nature of peer-to-peer networks, using a non-adaptive landmark selection algorithm may not be appropriate. In this paper, an adaptive landmark clustering algorithm obtained from the combination of mOverlay and learning automata is proposed. Learning automata are used to adaptively select appropriate landmark peers for the clusters in such a way that the total communication delay will be minimized. Simulation results have shown that the proposed algorithm outperforms the existing algorithms with respect to communication delay and average round-trip time between peers within clusters. peers in the overlay network. This graph is called as overlay graph. The topology of the underlay network can be represented by a graph G′ = (V′, E′) in which V′ = {position 1 , position 2 , …, position n } is a set of positions in the underlay network and E′ ⊆ V′ × V′ is a set of links connecting the positions in the underlay network. In peer-to-peer networks, each peer in V is mapped to a position in V′ according to a one-to-one function H : V → V′. In landmark clustering algorithms, the overlay graph is formed by clusters in which each cluster has a landmark peer. The peers that are not selected as landmark peers are called ordinary peers. The communication between ordinary peers should be handled by landmark peers. Let C i (where i ∈ {1, 2, 3, 4, …, p}) represent the set of p clusters in the network and L i ∈ V denote the landmark peer of cluster C i . The topology of the landmark peers network can be represented by a graph G l = (V l , E l ) in which V l ⊆ V is a set of landmark peers and E l ⊆ V × V is a set of links connecting the landmark peers in the overlay network. This graph is called landmark graph. In the peer-to-peer network that is constructed based on the landmark clustering algorithms, each landmark peer in V l is mapped to several ordinary peers in V according to a one-to-many function H′ : V l → V. Each landmark peer in V l is mapped to several positions in V′ using H′ and H. Figure 1 shows an example of a peer-to-peer network that uses two clusters to manage the overlay topology. In this example, peer m and peer j are two landmark peers.A primary goal of landmark clustering algorithms is to improve the performance of the overlay network in terms of communication dela...

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

Cited by 95 publications

References 36 publications

LACAIS: Learning Automata Based Cooperative Artificial Immune System for Function Optimization

LACAIS: Learning Automata Based Cooperative Artificial Immune System for Function Optimization

Adaptive cooperative particle swarm optimizer

A distributed adaptive landmark clustering algorithm based on mOverlay and learning automata for topology mismatch problem in unstructured peer‐to‐peer networks

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