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Weighted differential evolution algorithm for numerical function optimization: a comparative study with cuckoo search, artificial bee colony, adaptive differential evolution, and backtracking search optimization algorithms

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NEURAL COMPUTING & APPLICATIONS 2020年第8期32卷 3923-3937页

作者： Civicioglu, Pinar Besdok, Erkan Gunen, Mehmet Akif Atasever, Umit Haluk Erciyes Univ Fac Aeronaut & Astronaut Dept Aircraft Elect & Elect Kayseri Turkey Erciyes Univ Engn Fac Dept Geomat Engn Kayseri Turkey

In this paper, weighted differential evolution algorithm (WDE) has been proposed for solving real-valued numerical optimization problems. When all parameters of WDE are determined randomly, in practice, WDE has no control parameter but the pattern size. WDE can solve unimodal, multimodal, separable, scalable, and hybrid problems. WDE has a very fast and quite simple structure, in addition, it can be parallelized due to its non-recursive nature. WDE has a strong exploration and exploitation capability. In this paper, WDE's success in solving CEC' 2013 problems was compared to 4 different EAs (i.e., CS, ABC, JADE, and BSA) statistically. One 3D geometric optimization problem (i.e., GPS network adjustment problem) and 4 constrained engineering design problems were used to examine the WDE's ability to solve real-world problems. Results obtained from the performed tests showed that, in general, problem-solving success of WDE is statistically better than the comparison algorithms that have been used in this paper.

关键词： Cuckoo search algorithm artificial bee colony algorithm Differential evolution algorithm Backtracking search optimization Particle swarm optimization

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Co-evolving bee colonies by forager migration: A multi-swarm based artificial bee colony algorithm for global search space

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APPLIED MATHEMATICS AND COMPUTATION 2014年第0期232卷 216-234页

作者： Biswas, Subhodip Das, Swagatam Debchoudhury, Shantanab Kundu, Souvik Jadavpur Univ Dept Elect & Telecommun Engn Kolkata 700032 India Indian Stat Inst Elect & Commun Sci Unit Kolkata 700108 W Bengal India

Swarm intelligent algorithms focus on imitating the collective intelligence of a group of simple agents that can work together as a unit. Such algorithms have particularly significant impact in the fields like optimization and artificial intelligence (AI). This research article focus on a recently proposed swarm-based metaheuristic called the artificial bee colony (ABC) algorithm and suggests modification to the algorithmic framework in order to enhance its performance. The proposed ABC variant shall be referred to as Migratory Multi-swarm artificial bee colony (MiMSABC) algorithm. Different perturbation schemes of ABC function differently in varying landscapes. Hence to maintain the basic essence of all these schemes, MiMSABC deploys a multiple swarm populations that are characterized by different and unique perturbation strategies. The concept of reinitializing foragers around a depleted food source using a limiting parameter, as often used conventionally in ABC algorithms, has been avoided. Instead a performance based set of criteria has been introduced to thoroughly detect subpopulations that have shown limited progress to eke out the global optimum. Once failure is detected in a subpopulation provisions have been made so that constituent foragers can migrate to a better performing subpopulation, maintaining, however, a minimum number of members for successful functioning of a subpopulation. To evaluate the performance of the algorithm, we have conducted comparative study involving 8 algorithms for testing the problems on 25 benchmark functions set proposed in the Special Session on IEEE Congress on Evolutionary Competition 2005. Thorough a detailed analysis we have highlighted the statistical superiority of our proposed MiMSABC approach over a set of population based metaheuristics. (C) 2013 Elsevier Inc. All rights reserved.

关键词： artificial bee colony algorithm Foraging Population-based Metaheuristics Migration No Free Lunch theorem

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Research on WNN Modeling for Gold Price Forecasting Based on Improved artificial bee colony algorithm

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COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2014年第unknown期2014卷 270658-270667页

作者： Li, Bai Zhejiang Univ Sch Control Sci & Engn Hangzhou 310027 Zhejiang Peoples R China

Gold price forecasting has been a hot issue in economics recently. In this work, wavelet neural network (WNN) combined with a novel artificial bee colony (ABC) algorithm is proposed for this gold price forecasting issue. In this improved algorithm, the conventional roulette selection strategy is discarded. Besides, the convergence statuses in a previous cycle of iteration are fully utilized as feedback messages to manipulate the searching intensity in a subsequent cycle. Experimental results confirm that this new algorithm converges faster than the conventional ABC when tested on some classical benchmark functions and is effective to improve modeling capacity of WNN regarding the gold price forecasting scheme.

关键词： Prices artificial bee colony algorithm activity based costing iterative methods

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artificial bee colony algorithm for Two-dimensional Loading Capacitated Vehicle Routing Problem

Artificial Bee Colony Algorithm for Two-dimensional Loading ...

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20th International Annual Conference on Management Science and Engineering

作者： Wu Bin Cai Hong Cui Zhi-yong Nanjing Univ Technol Sch Econ & Management Nanjing 210009 Jiangsu Peoples R China

ISBN: (纸本)9781479904716

The artificial bee colony algorithm (ABC) with three loading heuristics for the two-dimensional loading capacitated vehicle routing problem (2L-CVRP) is presented in the paper. The 2L-CVRP is a combination of two well-known NP-hard problems, the capacitated vehicle routing problem, and the two-dimensional bin packing problem. It is very difficult to get a good performance solution in practice for these problems. The problem is solved by different heuristics for the loading part, and by artificial bee colony algorithm for the overall optimization. To solve the representation problem of the solution, a novel real encoding is presented to represent the solution for ABC. The effectiveness of the proposed algorithm is tested, and proven by extensive computational experiments on benchmark instances.

关键词： artificial bee colony algorithm 2L-CVRP bin packing problem vehicle routing problem

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Codebook design using improved particle swarm optimization based on selection probability of artificial bee colony algorithm

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Journal of Chongqing University 2014年第3期13卷 90-98页

作者：浦灵敏胡宏梅 Chien-shiung Institute of Technology

In the paper, a new selection probability inspired by artificial bee colony algorithm is introduced into standard particle swarm optimization by improving the global extremum updating condition to enhance the capability of its overall situation search. The experiment result shows that the new scheme is more valuable and effective than other schemes in the convergence of codebook design and the performance of codebook, and it can avoid the premature phenomenon of the particles.

关键词： vector quantization codebook design particle swarm optimization artificial bee colony algorithm

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artificial bee colony algorithm with Two-Stage Eagle Strategy

Artificial Bee Colony Algorithm with Two-Stage Eagle Strateg...

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9th International Conference on Computational Intelligence and Security (CIS)

作者： Jia, Ruimin He, Dengxu Guangxi Univ Nationalities Coll Informat Sci & Engn Nanning Peoples R China Guangxi Univ Nationalities Coll Sci Nanning Peoples R China

ISBN: (纸本)9781479925483

Pointing at that artificial bee colony algorithm (ABC) has the defect of slow search speed and low precision, the article proposed an Improved artificial bee colony algorithm with Two-Eagle Strategy (ETABC) through using a kind of optimization method-Eagle Strategy, and proved the convergence of ETABC. The simulation results show that ETABC is more effective in solving optimization problems.

关键词： Eagle Strategy artificial bee colony algorithm convergence cuckoo search

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Neural network based breast cancer detection using hybrid artificial bee colony and black hole algorithm

Neural network based breast cancer detection using hybrid ar...

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Advances in Computing, Communication Control and Networking (ICAC3N), International Conference on

作者： Indu Singh Mohit Dhalwal Himanshu Mridul Maurya Department of Computer Science and Engineering Delhi Technological University New Delhi India

When there is development of malignant tumor in breast notably in female is known as Breast cancer. Detection in the early stage of this disease is the only feasible solution. In this paper a new approach has been proposed to detect this disease in the early stage by employing neural networks and optimizing them with the help of a hybrid of artificial bee colony algorithm and black hole algorithm. This combination of neural networks with nature inspired algorithm is able to understand the deep patterns inside the dataset and produce great results. Using the proposed approach an accuracy of 98.94% is achieved on the Breast Cancer Wisconsin (Diagnostic) Data Set and as a result, is a trustworthy alternative to human experts when it comes to the classify the breast cancer tumors.

关键词： Malignant tumors Neural networks artificial bee colony algorithm Breast cancer Classification algorithms Task analysis

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artificial bee colony algorithm FOR POWER PLANT OPTIMIZATION

ARTIFICIAL BEE COLONY ALGORITHM FOR POWER PLANT OPTIMIZATION

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27th European Conference on Modelling and Simulation, ECMS 2013

作者： Biegler-Koenig, Friedrich Univ Appl Sci Dept Appl Math D-33615 Bielefeld Germany

ISBN: (纸本)9780956494467

The mathematical model of a fleet of power plants can be optimized with respect to energy production. This involves the solution of a mixed integer problem. Traditionally these problems are solved by linearization of the continuous and non-linear parts and subsequent application of a Simplex-type algorithm. In order to handle nonlinearities, so-called "biomimetic" optimization algorithms can be applied. As an example, we are proposing an approach to first model power plant blocks with fast Neural Networks. Afterwards, we optimize the operation of multi-block power plants over a period of time using an artificial bee colony algorithm.

关键词： artificial bee colony algorithm Evolutionary Methods Neural Networks Mixed Integer Optimization Power Plant Optimization

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Optimum Design of Fractional Order PID Controller for an AVR System Using an Improved artificial bee colony algorithm

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Acta Automatica Sinica 2014年第5期40卷 973-979页

作者： Dong-Li ZHANG Ying-Gan TANG Xin-Ping GUAN Institute of Electrical Engineering Yanshan University Qinhuangdao 066004 China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai 2002 40 China Institute of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai 200240 China

Fractional order proportional-integral-derivative (FOPID) controller generalizes the standard PID controller. Compared to PID controller, FOPID controller has more parameters and the tuning of parameters is more complex. In this paper, an improved artificial bee colony algorithm, which combines cyclic exchange neighborhood with chaos (CNC-ABC), is proposed for the sake of tuning the parameters of FOPID controller. The characteristic of the proposed CNC-ABC exists in two folds: one is that it enlarges the search scope of the solution by utilizing cyclic exchange neighborhood techniques, speeds up the convergence of artificial bee colony algorithm (ABC). The other is that it has potential to get out of local optima by exploiting the ergodicity of chaos. The proposed CNC-ABC algorithm is used to optimize the parameters of the FOPID controller for an automatic voltage regulator (AVR) system. Numerical simulations show that the CNC-ABC FOPID controller has better performance than other FOPID and PID controllers.

关键词： Fractional order proportional-integral-derivative (FOPID) controller optimal control artificial bee colony algorithm cyclic exchange neighborhood chaos automatic voltage regualator (AVR) system

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Comparison of Ant colony Optimization and artificial bee colony algorithms for Solving Electronic Support Search Dwell Scheduling Problem 15

Comparison of Ant Colony Optimization and Artificial Bee Col...

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15th Turkish National Software Engineering Symposium (UYMS)

作者： Erkut, Okan Hardalac, Firat ASELSAN AS Facil Radar & Elect Warfare Syst Ankara Turkey Gazi Univ Dept Elect Elect Engn Ankara Turkey

ISBN: (纸本)9781665410700

Electronic support search dwell scheduling is a combinatorial optimization problem which appears when the dwells overlapping on frequency domain are assigned to different receivers and required to be executed simultaneously by the receivers that are on a single electronic support platform. Overlapping the dwells on time domain provides sensor fusion ability and improves the detection performance of the system against target radar systems. In this paper, dwell sets of the receivers are scheduled to solve electronic support search dwell scheduling problem with two meta-heuristic swarm intelligence algorithms: Ant colony algorithm and artificial bee colony algorithm. Simulation results show that while both algorithms are capable of solving the problem, Ant colony algorithm provides a solution roughly 43% faster with a limit on number of iterations and artificial bee colony algorithm provides roughly 30% quicker and 26% more consistent solution with a limit on maximum acceptable cost.

关键词： Ant colony Optimization algorithm artificial bee colony algorithm Combinatorial Optimization Electronic Support Scheduling

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