A Double Evolutionary Learning Moth-Flame Optimization for Real-Parameter Global Optimization Problems

Li, Chunquan; Niu, Zheng; Song, Zhenshou; Li, Buxing; Jiang, Fan; Liu, Peter

doi:10.1109/access.2018.2884130

Cited by 47 publications

(32 citation statements)

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“…However, the new local search was carried out by applying a random point swaps in between the two randomly selected solutions using (14) and then evaluating both solutions after the swap. The application of the short-term memory shall help to increase the number of different exploited solutions; therefore, a solution quality is expected to be slightly higher.…”

Section: Improvement Stage 2 (Pseudo-code Line 34-48 Of Algorithm 2)mentioning

confidence: 99%

“…The tests were carried out by applying various strategies such as vector grouping learning scheme [13]; enhancement of the BSO ideas grouping and ideas generation mechanism [12]; multi-information interaction [11]; and simple individual updating [60]. The authors of [14] proposed a differential evolution flame generation strategy in an original moth-flame optimization algorithm (MFO) to obtain a sufficient population diversity and improve the algorithm's exploration performance. In [19], the researchers adopted the variable neighborhood search (VNS) to expand the intensification process and increase the population diversity of a hybrid discrete water wave optimization algorithm (HWWO).…”

Section: Introductionmentioning

confidence: 99%

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A Migration-Based Cuttlefish Algorithm With Short-Term Memory for Optimization Problems

2020

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Cuttlefish algorithm (CFA) is a metaheuristic bio-inspired algorithm that mimics the color-changing behavior by the cuttlefish. It is produced by light reflected from different layers of cells and involves two processes, i.e., reflection and visibility. The reflection process simulates the light reflection mechanism, while the visibility process simulates the visible appearance of the matching pattern used by the cuttlefish. There is no cooperation strategy between the solutions of the CFA's sub-populations. The strategy can improve the capabilities of local exploitation and global exploration in terms of solution diversity and quality during the search process. This paper introduces two schemes to improve the performance of the cuttlefish algorithm in continuous optimization problems. Firstly, a migration strategy is employed between the multi-population cuttlefish to increase solutions diversity during the search process. Secondly, one of the exploitation strategies of the standard cuttlefish is replaced with a new exploitation strategy based on short-term memory. The test demonstrates that the proposed algorithm outperforms the standard cuttlefish algorithm. Besides, the performance of the proposed algorithm was investigated using the CEC2013 benchmarking test functions. Comparisons with several state-of-the-art algorithms were performed, and the outcomes indicated that the proposed method offers a competitive performance advantage over the alternatives.

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Section: Improvement Stage 2 (Pseudo-code Line 34-48 Of Algorithm 2)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Migration-Based Cuttlefish Algorithm With Short-Term Memory for Optimization Problems

2020

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“…MFO has been also extended to multi-objective optimization problems such as the non-dominated sorting moth-flame optimization (NS-MFO) [41]. In addition, MFO was hybridized with other techniques, such as water cycle algorithm [42], differential evolution [43], and oppositionbased learning [39], in order to achieve better solutions for optimization problems. For a recent review for MFO variants and applications, the reader may refer to [44].…”

Section: Introductionmentioning

confidence: 99%

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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“…Although the introduction of agent model is helpful to improve group decision-making, users cannot directly exchange information, which limits the further improvement of collaboration quality. Besides, some scholars [23]- [27] combined swarm intelligence algorithm to solve the contradiction between improving the accuracy of adaptive value and reducing the burden of operation. Using two different evolutionary learning strategies, namely differential evolutionary flame generation and dynamic flame guidance, Li et al [27] designed a double-evolutionary learning algorithm for generating high-performance flame and dynamically guided moth search.…”

Section: Introductionmentioning

confidence: 99%

User Preference Adaptive Fitness of Interactive Genetic Algorithm Based Ceramic Disk Pattern Generation Method

Cai

2020

IEEE Access

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Pattern design in real life is a complex optimization challenge that objective function can not express explicitly, and can be classified as an implicit target optimization problem. Owing to the fact that the objective function in implicit target optimization problem can not be completely structured, the traditional intelligent decision optimization method is inapplicable. This paper proposes an interactive genetic algorithm based on user preference adaptive fitness, in which users actively participate in optimization and take their subjective evaluation value as the adaptive fitness of evolutionary individuals. On the basis of adjusting the tone coordination degree of ceramic disc pattern, the improved method gives the calculation of user interest degree and style similarity, and completes the establishment of user preference model. The individual similarity calculation of the algorithm integrates the user preference information, making the calculation result more objective. In the experiment, the grain genes of ceramic disc pattern can be divided into two types: main body and border, both of which can be encoded by 16-bit binary code strings. And the weight values of style, tone collocation and structure were set respectively, and the ceramic disc patterns of modern fashion and fresh styles were obtained. In addition, the algorithm performance comparison is also completed. The experimental results show that with the progress of evolution, the midpoint and average width of the fitness of the population decrease, and the population quality is improved. Our algorithm can generate ceramic disc patterns of different styles, and the algorithm can obtain more satisfactory solutions in less evolutionary generations, which speeds up the algorithm's convergence speed and shows better evolutionary optimization ability. INDEX TERMS Interactive genetic algorithm, user preferences, fitness, ceramic, design of disc. HUAFEI CAI received the M.S. and Ph.D. degrees from the

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A Double Evolutionary Learning Moth-Flame Optimization for Real-Parameter Global Optimization Problems

Cited by 47 publications

References 46 publications

A Migration-Based Cuttlefish Algorithm With Short-Term Memory for Optimization Problems

A Migration-Based Cuttlefish Algorithm With Short-Term Memory for Optimization Problems

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

User Preference Adaptive Fitness of Interactive Genetic Algorithm Based Ceramic Disk Pattern Generation Method

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