Cluster Guide Particle Swarm Optimization (CGPSO) for Underdetermined Blind Source Separation With Advanced Conditions

Sun, Tsung-Ying; Liu, Chan-Cheng; Tsai, Shang-Jeng; Hsieh, Sheng-Ta; Li, Kan-Yuan

doi:10.1109/tevc.2010.2049361

Cited by 42 publications

(20 citation statements)

References 39 publications

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“…In order to obtain better learning rate, therefore, the modified PSO is proposed to search for two optimal learning rates of the weights in the recurrent Laguerre orthogonal polynomials NN. The PSO [30][31][32][33][34][35][36][37][38][39][40][41][42][43], which has three parameters as two acceleration coefficients a 1 , a 2 and inertia weight σðh 1 Þ, has a significant impact on performance of the algorithm, especially the impact of inertia weight. The impact is different on different conditions and is also different at different times under the same condition.…”

Section: Remarkmentioning

confidence: 99%

“…Reducing inertia weight PSO is a topical algorithm, which inertia weight σðh 1 Þ decreases linearly from 0.9 to 0.4 [38,39]. Some scholars propose the increasing inertia weight PSO, which inertia weight σðh 1 Þ increases linearly from 0.4 to 0.9 [40,41]. αðh 1 Þ is the constriction factor introduced by Eberhart and Shi [38,39,43] for avoiding the swarm from premature convergence and to ensure stability of the system.…”

Section: Remarkmentioning

confidence: 99%

“…To determine the relationship between local search and global search, Shi et al [32,38,39] proposed an improved PSO involving the use of an inertial weight for controlling the exploitation and exploration. Some researchers [40][41][42][43] have proposed a partially improved particle swarm algorithm, called partial PSO, involving an inertia factor. PSO has the drawback of premature convergence, and therefore, in the current study, a modified PSO was proposed to prevent premature convergence and to achieve an optimal learning rate and faster convergence.…”

Section: Introductionmentioning

confidence: 99%

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RETRACTED: Novel application of continuously variable transmission system using composite recurrent Laguerre orthogonal polynomials modified PSO NN control system

Lin

2016

ISA Transactions

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Section: Remarkmentioning

confidence: 99%

Section: Remarkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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RETRACTED: Novel application of continuously variable transmission system using composite recurrent Laguerre orthogonal polynomials modified PSO NN control system

Lin

2016

ISA Transactions

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“…Swarm techniques are most applicable to very large sets of agents, in particular the use of particle swarm optimization. Topologically independent algorithms have been developed that apply both locally and globally [48] and PSO has interestingly been utilized in mixed signal analysis [37]. Swarm intelligence has also been applied to teams of robots [41], and several successful applications specific to multi-robot coordination include search and rescue [8,26,49], robotic soccer [19], mobile sensor networks [16,30], mine collection [11], and patrol with adversaries [2].…”

Section: Related Workmentioning

confidence: 99%

Dynamic Partnership Formation for Multi-Rover Coordination

Knudson

Tumer

2013

Advs. Complex Syst.

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Coordinating multiagent systems to maximize global information collection is a key challenge in many real world applications such as planetary exploration, and search and rescue. In particular, in many domains where communication is expensive (e.g., in terms of energy), the coordination must be achieved in a passive manner, without agents explicitly informing other agents of their states and/or intended actions. In this work, we extend results on such multiagent coordination algorithms to domains where the agents cannot achieve the required tasks without forming teams. We investigate team formation in three types of domains, one where n agents need to perform a task for the team to receive credit, one where there is an optimal number of agents (n) required for the task, but where the agents receive a decaying reward if they form a team with membership other than n, and finally we investigate heterogeneous teams where individuals vary in construction. Our results show that encouraging agents to coordinate is more successful than strictly requiring coordination. We also show that devising agent objective functions that are aligned with the global objective and locally computable significantly outperform systems where agents directly use the global objective, and that the improvement increases with the complexity of the task.

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“…Alcayde et al [22] considered the different goal of Pareto-based multi-objective strategies to generate a front (set) of non-dominated solutions as an approximation to the true Pareto-optimal front solution. Sun et al [23] proposed an algorithm in which a short-time Fourier transform is used to find a time-varying mixing matrix. Yao [24] used supply chain scheduling optimization in mass customization based on dynamic profit and cost to solve these constraints.…”

Section: Introductionmentioning

confidence: 99%

Global Search PSO to Analyze the Values of Cultural and Creative Industries

Wang¹,

Li²

2017

Open Cyber

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Background:Cultural and creative commodities and services are not evaluated by the service provider, but by the users. According to the preliminary interviews, it is incorrect to assume that the more expensive, the more consumers like the products. Objective:To analyze such commodities and services, this study employs a multi-objective planning model based on Value-Price-Cost theory to establish the relevant objectives and constraints. Method:A global search Particle Swarm Optimization (PSO) model is then established based on cluster analysis and grey theory. The three main operational mechanisms are: (1) an external repository to retain the optimal non-dominated solution set; (2) combined cluster analysis and grey theory to ensure a better distribution of the non-dominated solution search process; and (3) a virtual circle center, coupled with the existing positions of non-dominated solutions, to drive each particle of a multi-objective PSO (MOPSO) toward the optimal non-dominated solution. Results:The results of this research indicate that the proposed multi-objective algorithm outperforms both the conventional MOPSO and a non-dominated sorting genetic algorithm. Conclusion:The managerial implication is that high commodity prices increase the total project cost and extend the completion time, but reduce the customer's quality evaluation of the commodity.

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Cluster Guide Particle Swarm Optimization (CGPSO) for Underdetermined Blind Source Separation With Advanced Conditions

Cited by 42 publications

References 39 publications

RETRACTED: Novel application of continuously variable transmission system using composite recurrent Laguerre orthogonal polynomials modified PSO NN control system

RETRACTED: Novel application of continuously variable transmission system using composite recurrent Laguerre orthogonal polynomials modified PSO NN control system

Dynamic Partnership Formation for Multi-Rover Coordination

Global Search PSO to Analyze the Values of Cultural and Creative Industries

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