Open AccessJournal Article
Fruit fly optimization algorithm with adaptive mutation
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Experimental results show that the new algorithm has the advantages of better global searching ability,speeder convergence and more precise convergence.Abstract:
In order to overcome the problems of low convergence precision and easily relapsing into local extremum in basic fruit fly optimization algorithm(FOA),this paper presented an adaptive mutation fruit fly optimization algorithm(FOAAM).During the evolution,in the condition of basic FOA's trapping in local extremum judging from the population's fitness variance and the current optimal,first,it generated M current optimal replicates.Then,it disturbed replicates by a certain probability P Gauss mutation operator.Finally,it optimized mutated replicates again to jump out of local extremum and continue to optimize.Experimental results show that the new algorithm has the advantages of better global searching ability,speeder convergence and more precise convergence.read more
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Analysis of service satisfaction in web auction logistics service using a combination of Fruit fly optimization algorithm and general regression neural network
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Book ChapterDOI
Fruit Fly Optimization Algorithm
Bo Xing,Wen-Jing Gao +1 more
TL;DR: This chapter describes the general knowledge of the foraging behaviour of fruit flies and the fundamentals and performance of FFOA, and presents a novel optimization algorithm called fruit fly optimization algorithm (FFOA) which is inspired by the behaviour of Fruit flies.
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Journal ArticleDOI
An improved fruit fly optimization algorithm for solving traveling salesman problem
TL;DR: To address TSP effectively, three improvements are proposed in this paper to improve FOA: the vision search process is reinforced in the foraging behavior of fruit flies to improve the convergence rate of FOA, and an elimination mechanism is added to FOA to increase the diversity.