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Optimization Applications With Multimodal Test Function Comparison

Authors: Mukesh Kumar Gupta*1, Samar Wazir2 & Md. TabrezNafis3;

Optimization Applications With Multimodal Test Function Comparison

Abstract

This paper attempts to put up a general review of common knowledge of dis-similar types of the intelligent optimization process, controlling techniques and algorithms like that swarm optimization or PSO. In this algorithm, we are deploying two models called as global – best and local – best, and hence we consider these models as in multimodal test functions. At last, the studies and results show that the methods are very highly competitive and also can be used as a suitable another approach to solving the problem of multi-object optimization when dealing with multimodal functions. We study different uncertainties from present approaches to addressing them and relationship of different processes is discussed. At last, we would like to propose some promising points are suggested for future research purpose.

Keywords

Swarm optimization, multi-object optimization, cloud computing, genetic algorithms. General Terms Swarm intelligence, nature-inspired algorithm.

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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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