LCMFO: An Improved Moth-Flame Algorithm for Combinatorial Optimization Problems

AbdElhamid, Heba; Helmi, Ahmed; Ziedan, Ibrahim

doi:10.21817/ijet/2018/v10i6/181006091

Cited by 4 publications

(7 citation statements)

References 27 publications

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“…In order to improve the capabilities of MFO and to overcome the problem of entrapment at local minima/optima in the search space, several variants of the basic MFO algorithm are recently proposed. In this section, the LCMFO [34], EMFO [35], CMFO [36], and CLSGMFO [37] algorithms are reviewed.…”

Section: Variants Of Mfo Algorithmmentioning

confidence: 99%

“…The idea of applying crossover operator within MFO algorithm was firstly proposed by AbdElhamid et al [34]. The authors proposed an improved version of MFO called LCMFO where both Lévy-flight distribution function [47] and crossover function are included in the MFO algorithm.…”

Section: Lcmfomentioning

confidence: 99%

“…Several modified versions of the MFO algorithm have been proposed to enhance its ability against entrapment in local minima and slow convergence. For example, Lévy Crossover MFO (LCMFO) algorithm [34] employed Lévy-flight distribution function to initialize solutions and crossover function as an auxiliary search operator. Another example is the enhanced MFO (EMFO) [35] in which cultural learning and Gaussian mutation was applied to update solutions.…”

Section: Introductionmentioning

confidence: 99%

“…As mentioned above, many variants of MFO have been presented in literature to face the problem of local minima and early convergence. To the best of authors' knowledge, the work of Abdelhamid et al [34] is the first to consider applying MFO to PBP problems. However, in the current work, much better and time efficient version of MFO is introduced with higher immunity against premature convergence.…”

Section: Introductionmentioning

confidence: 99%

“…Hence this method is called smallest position values (SPV). To enhance the performance of MFO, the crossover function of genetic algorithms has been used by Abdelhamid et al [34] as a perturbation operator. In this paper, a similar approach is adopted with modifications.…”

Section: Introductionmentioning

confidence: 99%

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An enhanced Moth-flame optimization algorithm for permutation-based problems

Helmi

Alenany

2020

Evol. Intel.

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Moth-flame optimizer (MFO) is one of the recently proposed metaheuristic optimization techniques which has been successfully used in wide range of applications. However, there are two issues with the MFO algorithm. First, as a stochastic technique, MFO may prematurely converge at some local minima during the search process. Second, the original MFO was developed for continuous search space problems and is not directly applicable to, e.g., permutation-based problems (PBP). In this paper, a novel perturbation strategy is introduced to the MFO algorithm to avoid probable local minima regions. This strategy works as follows: if the best solution obtained so far doesn't improve for a given number of consecutive iterations, the current population of solutions is perturbed using some crossover mechanism as an attempt to explore new promising neighbourhoods in the search space. In addition, smallest position values mapping technique is employed in order for the proposed, termed CrossMFO (COMFO), algorithm to be applicable to PBP problems. It is noticed that, despite these modifications, the proposed COMFO has the same time complexity order as the original MFO. Extensive simulation experiments are conducted to compare the proposed COMFO to the MFO, other enhanced versions of MFO, and some metaheuristic optimizers in solving the well-known Travelling Salesman Problem (TSP). Empirical results show that the solutions obtained using MFO are improved by a factor of 24-47% on average for large TSP instances having more than 100 cities using COMFO and can even reach 38-58% using different settings. In addition, compared to other algorithms in the literature, the proposed algorithm provides, on average, better solutions. Hence, it can be considered a promising and efficient technique for this type of problems.

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