Death mechanism-based moth–flame optimization with improved flame generation mechanism for global optimization tasks

Li, Zhifu; Zeng, Junhai; Chen, YangQuan; Ma, Ge; Liu, Guiyun

doi:10.1016/j.eswa.2021.115436

Cited by 23 publications

(15 citation statements)

References 61 publications

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“…Moreover, the method of evolutionary population dynamics (EPD) is employed to address premature convergence and local optima stagnation. ODSFMFO, proposed by Li et al [98], is a hybridization of MFO with differential evolution (DE) and shuffled frog leaping algorithm (SFLA). In addition, the algorithm is enhanced by the addition of a flame generation strategy and death mechanism.…”

Section: Related Workmentioning

confidence: 99%

“…LGCMFO [96], SMFO [97], and ODSFMFO [98] in dimension 30. Then, the top four algorithms and eight other state-of-the-art swarm intelligence algorithms were considered for the main experiments.…”

Section: Introductionmentioning

confidence: 99%

“…Then, the top four algorithms and eight other state-of-the-art swarm intelligence algorithms were considered for the main experiments. Hence, the total competitors for the main experiments were KH [53], GWO [41], MFO [67], WOA [54], WCMFO [93], CMFO [94], HGSO [99], RGA-DX [50], ChOA [57], AOA [100], and ODSFMFO [98]. The experimental evaluations and statistical tests revealed that the M-MFO algorithm outperforms other competitor algorithms with overall effectiveness of 91%.…”

Section: Introductionmentioning

confidence: 99%

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Migration-Based Moth-Flame Optimization Algorithm

et al. 2021

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Moth–flame optimization (MFO) is a prominent swarm intelligence algorithm that demonstrates sufficient efficiency in tackling various optimization tasks. However, MFO cannot provide competitive results for complex optimization problems. The algorithm sinks into the local optimum due to the rapid dropping of population diversity and poor exploration. Hence, in this article, a migration-based moth–flame optimization (M-MFO) algorithm is proposed to address the mentioned issues. In M-MFO, the main focus is on improving the position of unlucky moths by migrating them stochastically in the early iterations using a random migration (RM) operator, maintaining the solution diversification by storing new qualified solutions separately in a guiding archive, and, finally, exploiting around the positions saved in the guiding archive using a guided migration (GM) operator. The dimensionally aware switch between these two operators guarantees the convergence of the population toward the promising zones. The proposed M-MFO was evaluated on the CEC 2018 benchmark suite on dimension 30 and compared against seven well-known variants of MFO, including LMFO, WCMFO, CMFO, CLSGMFO, LGCMFO, SMFO, and ODSFMFO. Then, the top four latest high-performing variants were considered for the main experiments with different dimensions, 30, 50, and 100. The experimental evaluations proved that the M-MFO provides sufficient exploration ability and population diversity maintenance by employing migration strategy and guiding archive. In addition, the statistical results analyzed by the Friedman test proved that the M-MFO demonstrates competitive performance compared to the contender algorithms used in the experiments.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Migration-Based Moth-Flame Optimization Algorithm

et al. 2021

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“…Ahmed et al [ 116 ] brought up DMFO-DE which is a discrete hybrid algorithm developed by integrating differential evolution and MFO to encounter the local optima problem and ameliorate the convergence speed and prevent the local optima problem. Li et al [ 117 ] proposed the ODSFMFO algorithm which consists of an improved flame generation mechanism based on opposition-based learning (OBL) and differential evolution (DE) algorithm, and an enhanced local search mechanism based on shuffled frog leaping algorithm (SFLA) and death mechanism.…”

Section: Related Workmentioning

confidence: 99%

An Improved Moth-Flame Optimization Algorithm with Adaptation Mechanism to Solve Numerical and Mechanical Engineering Problems

Nadimi-Shahraki

Fatahi

Zamani

et al. 2021

Entropy

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Moth-flame optimization (MFO) algorithm inspired by the transverse orientation of moths toward the light source is an effective approach to solve global optimization problems. However, the MFO algorithm suffers from issues such as premature convergence, low population diversity, local optima entrapment, and imbalance between exploration and exploitation. In this study, therefore, an improved moth-flame optimization (I-MFO) algorithm is proposed to cope with canonical MFO’s issues by locating trapped moths in local optimum via defining memory for each moth. The trapped moths tend to escape from the local optima by taking advantage of the adapted wandering around search (AWAS) strategy. The efficiency of the proposed I-MFO is evaluated by CEC 2018 benchmark functions and compared against other well-known metaheuristic algorithms. Moreover, the obtained results are statistically analyzed by the Friedman test on 30, 50, and 100 dimensions. Finally, the ability of the I-MFO algorithm to find the best optimal solutions for mechanical engineering problems is evaluated with three problems from the latest test-suite CEC 2020. The experimental and statistical results demonstrate that the proposed I-MFO is significantly superior to the contender algorithms and it successfully upgrades the shortcomings of the canonical MFO.

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“…In order to solve the problem that the original ALO is easy to fall into the local optimal solution, Dong et al used the dynamic opposite learning strategy and the dynamic random walk method based on dynamic random number to improve the ALO [ 39 ]. Because of the lack of population diversity and global search ability of the original MFO, Li et al applied the flame generation mechanism based on opposition-based learning and differential evolution algorithm and the local search mechanism based on shuffled frog leaping algorithm to the original MFO and proposed an improved MFO called ODSFMFO [ 40 ]. In order to enhance the performance of SCA in large-scale global optimization problems, Li et al proposed a dynamic sine cosine algorithm (DSCA) by designing nonlinear curves [ 41 ].…”

Section: Introductionmentioning

confidence: 99%

An improved African vultures optimization algorithm based on tent chaotic mapping and time-varying mechanism

Fan

Wang

2021

PLoS ONE

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Metaheuristic optimization algorithms are one of the most effective methods for solving complex engineering problems. However, the performance of a metaheuristic algorithm is related to its exploration ability and exploitation ability. Therefore, to further improve the African vultures optimization algorithm (AVOA), a new metaheuristic algorithm, an improved African vultures optimization algorithm based on tent chaotic mapping and time-varying mechanism (TAVOA), is proposed. First, a tent chaotic map is introduced for population initialization. Second, the individual’s historical optimal position is recorded and applied to individual location updating. Third, a time-varying mechanism is designed to balance the exploration ability and exploitation ability. To verify the effectiveness and efficiency of TAVOA, TAVOA is tested on 23 basic benchmark functions, 28 CEC 2013 benchmark functions and 3 common real-world engineering design problems, and compared with AVOA and 5 other state-of-the-art metaheuristic optimization algorithms. According to the results of the Wilcoxon rank-sum test with 5%, among the 23 basic benchmark functions, the performance of TAVOA has significantly better than that of AVOA on 13 functions. Among the 28 CEC 2013 benchmark functions, the performance of TAVOA on 9 functions is significantly better than AVOA, and on 17 functions is similar to AVOA. Besides, compared with the six metaheuristic optimization algorithms, TAVOA also shows good performance in real-world engineering design problems.

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Death mechanism-based moth–flame optimization with improved flame generation mechanism for global optimization tasks

Cited by 23 publications

References 61 publications

Migration-Based Moth-Flame Optimization Algorithm

Migration-Based Moth-Flame Optimization Algorithm

An Improved Moth-Flame Optimization Algorithm with Adaptation Mechanism to Solve Numerical and Mechanical Engineering Problems

An improved African vultures optimization algorithm based on tent chaotic mapping and time-varying mechanism

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