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Best neighbor-guided artificial bee colony algorithm for continuous optimization problems

SOFT COMPUTING 2019年第18期23卷 8723-8740页

作者： Peng, Hu Deng, Changshou Wu, Zhijian Jiujiang Univ Sch Informat Sci & Technol Jiujiang 332005 Peoples R China Wuhan Univ Sch Comp Wuhan 430072 Hubei Peoples R China

As a relatively recent invented swarm intelligence algorithm, artificial bee colony (ABC) becomes popular and is powerful for solving the tough continuous optimization problems. However, the weak exploitation has greatly affected the performance of basic ABC algorithm. Meanwhile, keeping a proper balance between the exploration and exploitation is critical work. To tackle these problems, this paper introduces a best neighbor-guided ABC algorithm, named NABC. In NABC, the best neighbor-guided solution search strategy is proposed to equilibrate the exploration and exploitation of new algorithm during the search process. Moreover, the global neighbor search operator has displaced the original random method in the scout bee phase aiming to preserve the search experiences. The experimental studies have been tested on a set of widely used benchmark functions (including the CEC 2013 shifted and rotated problems) and one real-world application problem (the software defect prediction). Experimental results and comparison with the state-of-the-art ABC variants indicate that NABC is very competitive and outperforms the other algorithms.

关键词： Artificial bee colony (ABC) continuous optimization problems Best neighbor-guided search Global neighbor search Software defect prediction

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A novel hybrid bat algorithm for solving continuous optimization problems

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APPLIED SOFT COMPUTING 2018年 73卷 67-82页

作者： Liu, Qi Wu, Lei Xiao, Wensheng Wang, Fengde Zhang, Linchuan China Univ Petr Coll Mech & Elect Engn Qingdao 266580 Peoples R China Nanyang Technol Univ Sch Civil & Environm Engn Maritime Inst NTU Singapore 639798 Singapore

The Bat Algorithm (BA), which is a global optimization method, performs poorly on complex continuous optimization problems due to BA's disadvantages such as the premature convergence problem. In this paper, we propose a novel Hybrid Bat Algorithm (HBA) to improve the performance of BA. Three modification methods are incorporated into the standard BA to enhance the local search capability and the ability to escape from local optimum traps. The effectiveness and contribution of these three modification methods are analyzed by using classical benchmark functions. Moreover, the performance of HBA is evaluated on the numerical functions from the CEC 2014 test suite and compared with those of wellknown optimization algorithms. The statistical test results indicate that HBA is a significant improvement. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Hybrid algorithm Bat algorithm Extremal optimization continuous optimization problems

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An improved artificial bee colony algorithm based on mean best-guided approach for continuous optimization problems and real brain MRI images segmentation

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NEURAL COMPUTING & APPLICATIONS 2021年第5期33卷 1671-1697页

作者： Alrosan, Ayat Alomoush, Waleed Norwawi, Norita Alswaitti, Mohammed Makhadmeh, Sharif Naser Univ Sains Islam Malaysia Dept Comp Sci Fac Sci & Technol Nilai Negeri Sembilan Malaysia Imam Abdulrahman Bin Faisal Univ Dept Comp Dammam Saudi Arabia Xiamen Univ Malaysia Sch Elect & Comp Engn ICT Sepang 43900 Selangor Darul Malaysia Univ Sains Malaysia Sch Comp Sci George Town 11800 Malaysia

The artificial bee colony (ABC) algorithm is a relatively new algorithm inspired by nature and has been shown to be efficient in contrast to other optimization algorithms. Nonetheless, ABC has some similar drawbacks to the optimization algorithms in terms of the unbalanced search behavior. The original ABC algorithm shows strong exploration capability with ineffective exploitation due to the unbalanced search model. In this paper, a new ABC algorithm called MeanABC is introduced to achieve the search behavior balance via a modified search equation based on the information of the mean of the previous best solutions. To evaluate the performance of the proposed algorithm, experiments were divided into two parts: First, the proposed algorithm was tested on a comprehensive set of 14 benchmark functions. The results show that the proposed MeanABC enhances the performance of the original ABC in terms of faster global convergence speed, solution quality, and better robustness when compared to other ABC variants. Secondly, the proposed algorithm was applied as a hybrid with the FCM algorithm as a segmentation technique to a set of 20 volumes of real brain MRI images with 20 images for each volume. All of these images have several characteristics, levels of difficulty, and cover different domains. The obtained results are promising, especially when the performance of the proposed algorithm was compared to other state-of-the-art segmentation techniques.

关键词： optimization algorithms Artificial bee colony continuous optimization problems Search behavior MRI images

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A novel real-coded population-based extremal optimization algorithm with polynomial mutation: A non-parametric statistical study on continuous optimization problems

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NEUROCOMPUTING 2016年第PartB期174卷 577-587页

作者： Li, Li-Min Lu, Kang-Di Zeng, Guo-Qiang Wu, Lie Chen, Min-Rong Wenzhou Univ Dept Elect & Elect Engn Wenzhou 325035 Peoples R China S China Normal Univ Sch Comp Guangzhou 510631 Guangdong Peoples R China

As a recently developed optimization method inspired by far-from-equilibrium dynamics of self-organized criticality, extremal optimization (EO) has been successfully applied to a variety of combinatorial optimization problems while its applications in continuous optimization problems are relatively rare. Additionally, there are only few studies concerning the effects of mutation operation on EO algorithms although mutation operation plays a crucial role in controlling the optimization dynamics and consequently affecting the performance of EO-based algorithms. This paper proposes a novel real-coded population-based EO algorithm with polynomial mutation (RPEO-PLM) for continuous optimization problems. The basic idea behind RPEO-PLM is the population-based iterated optimization consisting of generation of a real-coded random initial population, evaluation of individual and population fitness, generation of a new population based on polynomial mutation, and updating the population by accepting the new population unconditionally. One of the most attractive advantages is its relative simplicity compared with other popular evolutionary algorithms due to its fewer adjustable parameters needing to be tuned and only selection and mutation operations. Furthermore, the experimental results on a large number of benchmark functions with the different dimensions by using non-parametric statistical tests including Friedman and Quade tests have shown that the proposed RPEO-PLM algorithm outperforms other popular population-based evolutionary algorithms, e.g., real-coded genetic algorithm (RCGA) with adaptive directed mutation (RCGA-ADM), RCGA with polynomial mutation (RCGA-PLM), intelligent evolutionary algorithm (IEA), a hybrid particle swarm optimization and EO algorithm (PSO-EO), the original population-based EO (PEO), and an improved RPEO algorithm with random mutation (IRPEO-RM) in terms of accuracy. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Extremal optimization Evolutionary algorithms Mutation continuous optimization problems Non-parametric statistical tests

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A self adaptive hybrid enhanced artificial bee colony algorithm for continuous optimization problems

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BIOSYSTEMS 2015年 132卷 43-53页

作者： Shan, Hai Yasuda, Toshiyuki Ohkura, Kazuhiro Hiroshima Univ Grad Sch Engn Higashihiroshima 739852 Japan

The artificial bee colony (ABC) algorithm is one of popular swarm intelligence algorithms that inspired by the foraging behavior of honeybee colonies. To improve the convergence ability, search speed of finding the best solution and control the balance between exploration and exploitation using this approach, we propose a self adaptive hybrid enhanced ABC algorithm in this paper. To evaluate the performance of standard ABC, best-so-far ABC (BsfABC), incremental ABC (IABC), and the proposed ABC algorithms, we implemented numerical optimization problems based on the IEEE Congress on Evolutionary Computation (CEC) 2014 test suite. Our experimental results show the comparative performance of standard ABC, BsfABC, IABC, and the proposed ABC algorithms. According to the results, we conclude that the proposed ABC algorithm is competitive to those state-of-the-art modified ABC algorithms such as BsfABC and IABC algorithms based on the benchmark problems defined by CEC 2014 test suite with dimension sizes of 10, 30, and 50, respectively. (C) 2015 Elsevier Ireland Ltd. All rights reserved.

关键词： Artificial bee colony algorithm Swarm intelligence continuous optimization problems Self adaptive mechanism CEC 2014 test suite

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An Enhanced Bat Algorithm with Random Walk for Solving continuous optimization problems 20

An Enhanced Bat Algorithm with Random Walk for Solving Conti...

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20th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD 2019)

作者： Tansui, Daranat Thammano, Arit Prince Songkla Univ Fac Humanities & Social Sci Pattani Campus Pattani 94000 Thailand King Mongkuts Inst Technol Ladkrabang Fac Informat Technol Computat Intelligence Lab Bangkok 10520 Thailand

ISBN: (纸本)9781728116518

Bat algorithm (BA) has been a successful algorithm for continuous optimization problems. However, its exploitation and exploration powers still leaves something to be desired. This work enhanced BA's exploitation power by introducing two new random walk processes that made its local search more thorough and enhanced it's exploration power by introducing inertia weight to intensify its global search near the end of the optimization process. We called this new algorithm an enhanced BA. The performance of enhanced BA on 15 widely accepted benchmark functions was compared with those of the original BA and genetic algorithm and found to be better than those achieved by the original BA and genetic algorithm on most of those benchmark functions. The directions of our future works are toward applying this enhanced BA to practical, real-world engineering problems and toward hybridizing BA with some other meta-heuristic algorithms.

关键词： Bat Algorithm Random Walk Inertia Weight continuous optimization problems

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A Look-Ahead Based Meta-heuristics for Optimizing continuous optimization problems 1st

A Look-Ahead Based Meta-heuristics for Optimizing Continuous...

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1st International Conference on optimization, Learning Algorithms and Applications (OL2A)

作者： Nordli, Thomas Bouhmala, Noureddine Univ South Eastern Norway Kongsberg Norway

In this paper, the famous kernighan-Lin algorithm is adjusted and embedded into the simulated annealing algorithm and the genetic algorithm for continuous optimization problems. The performance of the different algori... 详细信息

ISBN: (纸本)9783030918859;9783030918842

关键词： continuous optimization problems Simulated annealing Genetic algorithm Local search

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Metaheuristic-Boltzmannian optimization model: A new methodology for convergence using the Jensen-Shannon metric in continuous optimization problems

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SWARM AND EVOLUTIONARY COMPUTATION 2022年 75卷

作者： de Anda-Suarez, Juan Calzada-Ledesma, Valentin Ortiz-Aguilar, Lucero Tecnol Nacl Mexico Inst Tecnol Super Purisima Rincon Purisima Del Rincon 36425 Mexico

We present a new methodology called J-SIGMA to infer state change information using the minimum Jensen- Shannon distance to describe a continuous optimization problem as a Boltzmannian system, as well as its applications to population-based metaheuristics. In general, a Boltzmannian system describes a macrostate from the statistics of the constituent microstate where the minimum energy is the most probable. If we model an optimization problem as a Boltzmann process, the global optimum would be the most probable state. To achieve this, we propose an analytical derivation of the minimum distance to the Boltzmann distribution using the parameters and to fit this model. As a case study, we implemented the J-SIGMA methodology on three families of population-based metaheuristics: Swarm, Evolutionary, and Estimation of Distribution, and used a set of continuous optimization functions from CEC'17 to evaluate their performance against other metaheuristics of each family. Finally, from a statistical analysis of the convergence performance, the evidence is shown to affirm that the J-SIGMA methodology can significantly improve the convergence performance of the algorithms, regardless of the metaheuristic family to which it belongs.

关键词： Jensen-Shannon metric Boltzmann system Population-based algorithms Particle Swarm optimization Differential Evolution Estimation of distribution algorithms continuous optimization problems

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IFA-EO: An improved firefly algorithm hybridized with extremal optimization for continuous unconstrained optimization problems

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SOFT COMPUTING 2023年第6期27卷 2943-2964页

作者： Chen, Min-Rong Yang, Liu-Qing Zeng, Guo-Qiang Lu, Kang-Di Huang, Yi-Yuan South China Normal Univ Sch Comp Sci Guangzhou 510631 Peoples R China Guangzhou Coll Commerce Sch Informat Technol & Engn Guangzhou 511363 Peoples R China Wenzhou Univ Natl Local Joint Engn Lab Digitalize Elect Design Wenzhou 325035 Peoples R China Jinan Univ Coll Cyber Secur Guangzhou 510632 Peoples R China Zhejiang Univ Inst Cyber Syst & Control Hangzhou 310018 Peoples R China

As one of the evolutionary algorithms, firefly algorithm (FA) has been widely used to solve various complex optimization problems. However, FA has significant drawbacks in slow convergence rate and is easily trapped into local optimum. To tackle these defects, this paper proposes an improved FA combined with extremal optimization (EO), named IFA-EO, where three strategies are incorporated. First, to balance the tradeoff between exploration ability and exploitation ability, we adopt a new attraction model for FA operation, which combines the full attraction model and the single attraction model through the probability choice strategy. In the single attraction model, small probability accepts the worse solution to improve the diversity of the offspring. Second, the adaptive step size is proposed based on the number of iterations to dynamically adjust the attention to the exploration model or exploitation model. Third, we combine an EO algorithm with powerful ability in local-search into FA. Experiments are tested on two group popular benchmarks including complex unimodal and multimodal functions. Our experimental results demonstrate that the proposed IFA-EO algorithm can deal with various complex optimization problems and has similar or better performance than the other eight FA variants, three EO-based algorithms, and one advanced differential evolution variant in terms of accuracy and statistical results.

关键词： Firefly algorithm extremal optimization Probability choice strategy Adaptive step size continuous optimization problems

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An Unsupervised Neural Network Algorithm SOMO for continuous optimization problems

An Unsupervised Neural Network Algorithm SOMO for Continuous...

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2018 3rd International Workshop on Materials Engineering and Computer Sciences(IWMECS 2018)

作者： Anzhi Qi Liaoning Jianzhu Vocational College

The application of self-organizing neural network in customer classification is *** concept of customer classification,index selection,classification method selection and SOM(Self Organization Map) clustering method are discussed,and a customer classification method based on SOM,Namely,the indexes of RFM(Recency;Frequency,Value,Monentary) are given,and the customers are classified according to the calculation of the comprehensive index and the relative learning results of each index,and the simulation calculation is performed,The simulation results are categorized to validate the algorithm.

关键词： SOMO continuous optimization problems Unsupervised Neural Network Algorithm

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