NDLI: An Improved Fitness Based Differential Evolution Algorithm (IFBDEA)

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An Improved Fitness Based Differential Evolution Algorithm (IFBDEA)

Content Provider	ACM Digital Library
Author	Meena, Prakash Sharma, V. P. Sharma, Akrati Sharma, Hitesh
Abstract	Research workers solve the simple problems of optimization by using various mathematical techniques. But to solve complex problems of optimization a stochastic and population based algorithm named Differential Evolution (DE) is used. DE is fast, simple and straightforward algorithm to optimize the problems which are complex. Like other evolutionary algorithms DE also has some drawbacks. In FBDE author tried to overcome these drawbacks and tested on some benchmark functions and real world problems. FBDE is also found good as compared to DE and its recent variants. The proposed algorithm is a improvement of FBDE using my previously published algorithm PGLFDE. In PGLFDE to make the balance between DE's exploration (globally) and exploitation (locally) capabilities levy flight search is modified using different mutation strategies for different population. In proposed algorithm we are hybridizing the PGLFDE into the FBDE to improve the accuracy, efficiency and reliability of FBDE and its name is called IFBDEA. We have plotted the graph between different points of crossover rates of IFBDEA and the sum of success rates of the problems taken. The proposed algorithm has been tested on 23 standard benchmark problems which includes real world problems and personified with DE, LFDE and FBDE algorithms.
Starting Page	1
Ending Page	6
Page Count	6
File Format	PDF
ISBN	9781450339629
DOI	10.1145/2905055.2905160
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-03-04
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Fitness based algorithm Differential evolution Population or generation based algorithm Local search strategy Standard problems
Content Type	Text
Resource Type	Article

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