A Novel Hybrid Crossover based Artificial Bee Colony Algorithm for Optimization Problem

Kumar, Sandeep; Sharma, Vivek; Kumari, Rajani

doi:10.5120/14136-2266

Cited by 22 publications

(8 citation statements)

References 21 publications

(26 reference statements)

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“…A crossover or mutation can function as an exploration or exploitation operator [3], [4]. Although optimization algorithms with higher degree of exploitation may have higher convergence speed, the challenge lies in locating the optimal solution and chances are it may not get past a local optimum.…”

Section: Achieving Exploration and Exploitation In Genetic Algorithmmentioning

confidence: 99%

Crossover and Mutation Operators of Genetic Algorithms

Lim¹,

Sultan²,

Sulaiman³

et al. 2017

IJMLC

107

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Abstract-Genetic algorithms (GA) are stimulated by population genetics and evolution at the population level where crossover and mutation comes from random variables. The problems of slow and premature convergence to suboptimal solution remain an existing struggle that GA is facing. Due to lower diversity in a population, it becomes challenging to locally exploit the solutions. In order to resolve these issues, the focus is now on reaching equilibrium between the explorative and exploitative features of GA. Therefore, the search process can be prompted to produce suitable GA solutions. This paper begins with an introduction, Section 2 describes the GA exploration and exploitation strategies to locate the optimum solutions. Section 3 and 4 present the lists of some prevalent mutation and crossover operators. This paper concludes that the key issue in developing a GA is to deliver a balance between explorative and exploitative features that complies with the combination of operators in order to produce exceptional performance as a GA as a whole.Index Terms-Crossover operator, mutation operator, exploitation, exploration. I. INTRODUCTIONThe main search operator in Genetic algorithms (GA) is the crossover operator which equally as significant as mutation, selection and coding in GA. The crossover operator functions primarily in the survey of information that is accessible through the search space, which inadvertently improves the behavior of the GA. On another note, mutation is a secondary operator. It functions to alter the genes of the offspring. A mutator will diversify the existing population and this inadvertently allows GAs to exploit promising areas of the search space thus avoiding local solutions [1]. Some of the mutation operators are designed to explicitly overcome certain types of issues over others [2]. The performance among all the comparative of GA operators are easily validated and compared through unbiased test problems from the literature, which are diverse in properties in terms of complexity and modality. This study substantially contributes in reviewing some prevalent mutation and crossover operators. The operators maintain a good balance between explorative and exploitative strategies while manufacturing the optimum GA solutions. II. ACHIEVING EXPLORATION AND EXPLOITATION IN GENETIC ALGORITHMA crossover or mutation can function as an exploration or exploitation operator [3], [4]. Although optimization algorithms with higher degree of exploitation may have Manuscript received August 30, 2016; revised December 8, 2016. Siew Mooi Lim is with University Malaysia of Computer Science and Engineering, Malaysia (e-mail: limsm66@gmail.com).higher convergence speed, the challenge lies in locating the optimal solution and chances are it may not get past a local optimum. On the other hand, algorithms that favor exploration over exploitation might consume more time in locating the global optimum, that is, coincidentally, due to its less sophisticated candidate solutions. A comprehensive survey in exploratio...

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Section: Achieving Exploration and Exploitation In Genetic Algorithmmentioning

confidence: 99%

Crossover and Mutation Operators of Genetic Algorithms

Lim¹,

Sultan²,

Sulaiman³

et al. 2017

IJMLC

107

View full text Add to dashboard Cite

show abstract

“…Qingxian and Haijun [24] employed the Boltzmann selection scheme and introduced an improved initialization scheme to improve the convergence speed. The hybrid crossover based ABC (CbABC) [25] is an exploitative variant that strengthens the exploitation phase of ABC by using a crossover operation.…”

Section: Existing Variants Of the Abc Algorithmmentioning

confidence: 99%

Adaptive Mutation Rate for the Artificial Bee Colony Algorithm: A Case Study on Benchmark Continuous Optimization Problems

ShabnamHasan¹

2015

IJAIS

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A major problem with the Artificial Bee Colony (ABC) algorithm is its premature convergence to the locally optimal points of the search space, which often originates from the lack of explorative search capability of its mutation operator. This paper introduces ABC with Adaptive Mutation Rate (ABC-AMR), a novel algorithm that modifies the basic mutation operation of the original ABC algorithm in an explorative way. The novelty of the proposed algorithm lies in an adaptive mutation strategy that enables ABC-AMR to automatically adjust the mutation rate, separately for each candidate solution of the bee population, in order to customize the degree of explorations and exploitations around each candidate solution, while the original ABC algorithm employs a naïve fixed mutation rate. Besides, a few more explorative schemes and parameter values are employed by ABC-AMR to assist the adaptive mutation procedure. ABC-AMR is evaluated on several benchmark numerical optimization problems and results are compared with the basic ABC algorithm. Results show that ABC-AMR can perform better optimization than the original ABC algorithm on some of the benchmark problems.

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“…Recent research shows that application area of ABC is very wide. A number of modifications in ABC [39]- [43] and hybridization of ABC with other metaheuristics are available in literature for example memetic algorithms incorporated in ABC [5]- [20], hybrid of ABC with genetic algorithms [37]- [38] and levy flight also incorporated in ABC [33].…”

Section: Introductionmentioning

confidence: 99%

A novel hybrid memetic search in Artificial Bee Colony algorithm

Kumar

Sharma

et al. 2014

2014 Seventh International Conference on Contemporary Computing (IC3)

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Artificial Bee Colony (ABC) algorithm is a Nature Inspired Algorithm (NIA). ABC is motivated by clever food foraging behavior of honey bees. Comparable to other population based stochastic algorithm ABC also lack balance in exploration of local search reason and exploitation of finest possible solutions in search space. The proposed algorithm is a hybrid of two memetic algorithms; it combines Levy Flight search in ABC (LFABC) and Memetic Search in ABC (MeABC) algorithm. The proposed algorithm named as Levy Flight Memetic Search in ABC (LFMABC) algorithm.The proposed LFMABC algorithm is tested over eleven benchmark functions in addition to two real world problems in order to establish its superiority over ABC and its recent variants. Results shows that proposed strategy LFMABC is able to find optimum in less time for considered problems.

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A Novel Hybrid Crossover based Artificial Bee Colony Algorithm for Optimization Problem

Cited by 22 publications

References 21 publications

Crossover and Mutation Operators of Genetic Algorithms

Crossover and Mutation Operators of Genetic Algorithms

Adaptive Mutation Rate for the Artificial Bee Colony Algorithm: A Case Study on Benchmark Continuous Optimization Problems

A novel hybrid memetic search in Artificial Bee Colony algorithm

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