Chaos Adaptive Improved Particle Swarm Algorithm for Solving Multi-Objective Optimization

Liu, Bing Xiang; Wu, Yan; Xu, Xing; Hu, Na; Cheng, Xiang

doi:10.11591/telkomnika.v12i1.3189

Cited by 5 publications

(4 citation statements)

References 15 publications

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“…Therefore, it is of paramount importance to focus on security challenge, which consequently improve the degree of trustworthiness of candidate resources. As a recommendation for the reported QoS challenges, the multi-criteria based cost optimization should be focused on providing optimal solutions to WFS problems [26,[109][110][111]. Also, researchers ought to give more attention to hybrid approaches by combining the strengths of existing approaches to provide more cost-effective solutions to WFS problems.…”

Section: Discussionmentioning

confidence: 99%

Cost-aware challenges for workflow scheduling approaches in cloud computing environments: Taxonomy and opportunities

Alkhanak

Lee

Khan

2015

Future Generation Computer Systems

140

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Section: Discussionmentioning

confidence: 99%

Cost-aware challenges for workflow scheduling approaches in cloud computing environments: Taxonomy and opportunities

Alkhanak

Lee

Khan

2015

Future Generation Computer Systems

140

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“…In this work, we present a modified Multi-Objective Particle Swarm Optimization (MOPSO), which allows the PSO algorithm to be able to solve multi-objective optimization problems. Our current work is an improvisation of the algorithm, in which we have added a constraint-handling mechanism and a mutation operator [13][14] that considerably improves the exploratory capabilities of the original algorithm [13,[15][16].…”

Section: Multi-objective Particle Swarm Optimization Algorithm (Mopso)mentioning

confidence: 99%

“…The above stated heuristic algorithms have overcome the drawbacks in the traditional methods, but also have certain 1089 limitations, that is they easily get trapped in the local optima and premature convergence would occur. In order to focus on a better convergence, the problem formulation was formulated as multi-objective optimization problem for different power system problems [14][15].…”

Section: Introductionmentioning

confidence: 99%

Optimal volt/var control of distribution system using MOPSO

Kumar

Sreekanth

2019

IJEECS

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This paper presents a novel method for solving multi-objective Volt/Var control of radial distribution system. The Volt/Var control is formulated as a multi-objective optimization problem which consists of the following objectives: minimization of real power loss, minimization of total voltage deviation and minimization of number of OLTC’s and capacitor operation and voltage fluctuations for a day-a-head in Distribution system.The Proposed MOPSO Algorithm is used to find the optimal settings of control variables such as On-Load tap changer (OLTC) and Shunt Capacitor. The proposed MOPSO algorithm is tested on a standard IEEE33-bus and 69-bus distribution system.

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“…One of the meta-heuristic methods that can be employed to overcame the deficiency of those methods is Particle Swarm Optimization (PSO). PSO has shown a promising optimization method to solve a complex problem such as power system [8], electronic industry, wireless sensor network, feature selection [9], circuit design [10], multi-objective optimization [11], and determining neuron weights in fuzzy neural networks [12].…”

Section: Introductionmentioning

confidence: 99%

Good Parameters for PSO in Optimizing Laying Hen Diet

Alfarisy¹,

Mahmudy²,

Natsir³

2018

IJECE

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Manual formulation of poultry diet by taking into account the fulfillment of all nutrients requirement with least cost is a difficult task. Particle Swarm Optimization (PSO) shows promising technique to solve this problem. However, there is a lack of studying a good parameter for PSO to solve feed formulation problem since PSO is sensitive to control parameter which depends on the problem. Therefore, this study investigates good swarm size, total iterations, acceleration coefficients, and inertia weight to produce a better formula. PSO with proposed good parameters is compared with other parameters. The obtained result shows that PSO with good parameters choice produces the highest fitness. Furthermore, good parameters of PSO can be used as a reference for a software developer and for further research to optimize poultry diet using PSO.

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Chaos Adaptive Improved Particle Swarm Algorithm for Solving Multi-Objective Optimization

Cited by 5 publications

References 15 publications

Cost-aware challenges for workflow scheduling approaches in cloud computing environments: Taxonomy and opportunities

Cost-aware challenges for workflow scheduling approaches in cloud computing environments: Taxonomy and opportunities

Optimal volt/var control of distribution system using MOPSO

Good Parameters for PSO in Optimizing Laying Hen Diet

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