Performance Analysis of Variants of Differential Evolution on Multi-Objective Optimization Problems

Aswani,; Praveen, V.; Thangavelu, S.

doi:10.17485/ijst/2015/v8i17/65727

Cited by 8 publications

(3 citation statements)

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“…Active research on DE for the past two decades has yielded numerous DE variants summarized in [37]. An empirical study on the performance of DE variants was studied by Praveen et al [38]. Mixing of DE variants affords promising options to solve optimization problems, yielding vitreous results [39][40][41][42].…”

Section: The Classical Dementioning

confidence: 99%

A Hybrid Multi-Population Reinitialization Strategy to Tackle Dynamic Optimization Problems

Raghul,

Jeyakumar

2023

IEEE Access

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In conventional optimization problems, it is assumed that all relevant parametric constraints remain stationary. In contrast, optimization problems encountered in practical applications are dynamic and supervened by uncertainties. The research community has evinced a keen interest in multi-population approaches combined with nature-inspired algorithms to manage dynamic optimization problems efficiently. Applying multi-population approaches to solve dynamic optimization problems engenders specific vital issues, such as reproducing sub-populations in new environments influenced by archival information. Moreover, overpartitioning the population may lead to aberrant utilization of computational resources among the subpopulations. These impediments are addressed using the proposed hybrid multi-population reinitialization strategy, which is a combination of distributed differential evolution algorithmic framework and reinitialization strategy. This scheme relies on simple reinitialization to surmount the dynamism. This framework was assessed on different instances in a moving peak benchmark problem, a proven benchmarking function in the domain of dynamic optimization. Furthermore, this study encompasses a comparative and statistical analysis to validate the effectiveness of the proposed approach in comparison to cutting-edge algorithms in solving dynamic optimization problems efficiently. The experimental results consistently show that the hybrid multi-population reinitialization strategy outperforms conventional Differential Evolution algorithms across various parameter configurations. This hybrid multi-population reinitialization strategy showcases its effectiveness in the successful handling of increased shift lengths and number of peaks, which are pivotal parameters in solving moving peak benchmark function.

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Section: The Classical Dementioning

confidence: 99%

A Hybrid Multi-Population Reinitialization Strategy to Tackle Dynamic Optimization Problems

Raghul,

Jeyakumar

2023

IEEE Access

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“…We proposed a new protocol to improve existing on-demand multiple path routing protocols by constructing multiple backup routes using DE algorithm in the last section. By using DE (Aswani et al, 2015), we obtain the optimal path from source to destination, and in the same phase we find an alternate path to be used in link failure.…”

Section: Backup Routing Using Dementioning

confidence: 99%

A differential evolution-based routing algorithm for multi-path environment in mobile ad hoc network

Sharma¹,

Sinha²

2019

IJHI

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Optimisation is a procedure through which the best possible ways of decision variables are obtained under the given set of constraints and in accordance to a selected optimisation objective function. Over the last decade, differential evolution (DE) algorithms have been extensively used in various problem domains and succeeded effectively in finding the optimal solutions. The present paper emphasis on a DE algorithm-based ad hoc on-demand multi-path distance vector (DE_AOMDV) protocol for mobile ad hoc network (MANET). The proposed DE_AOMDV routing protocol has better performance and improves the quality of the MANETs. The main objective of this research paper is to find the optimal path from available multiple paths between source and destination to be used in route recovery process. DE_AOMDV provides backup paths to avoid reroute discovery in the case of link failure between nodes. Simulation based results and data analysis shows DE_AOMDV protocol is better than AOMDV.

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“…Each weight should be chosen in a way that sum of weights should be equal to 1 and any weight should lie between 0 and 1. It is generally represented by the equation (5)…”

Section: A) Weighted Summentioning

confidence: 99%

Scalarizing functions in solving multi-objective problem-an evolutionary approach

Vasumathi¹,

Thangavelu²

2019

IJEECS

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<p><span>Scalarizing functions had long been observed for optimization of multi-objective problems. Scalarizing functions on multi-objective problem along with Differential Evolution (DE) algorithm variants had been used to analyze the effect of scalarizing functions. The main purpose is to find the better scalarizing function which can be applied for optimization. The effective solution of the multi-objective problem depends on the various factors like the DE algorithm and the scalarizing functions used. Multi objective evolutionary algorithm (MOEA) framework in java had been used for performing the analysis. The Obtained results showed that Tchebysheff scalarization function performs better than the other scalarizing functions on various indicator functions used.</span></p>

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Performance Analysis of Variants of Differential Evolution on Multi-Objective Optimization Problems

Cited by 8 publications

References 0 publications

A Hybrid Multi-Population Reinitialization Strategy to Tackle Dynamic Optimization Problems

A Hybrid Multi-Population Reinitialization Strategy to Tackle Dynamic Optimization Problems

A differential evolution-based routing algorithm for multi-path environment in mobile ad hoc network

Scalarizing functions in solving multi-objective problem-an evolutionary approach

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