Hybrid Artificial Bee Colony algorithm with Differential Evolution

Jadon, Shimpi Singh; Tiwari, Ritu; Sharma, Harish; Bansal, Jagdish Chand

doi:10.1016/j.asoc.2017.04.018

Cited by 162 publications

(55 citation statements)

References 19 publications

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“…DE/best/2 is utilized in this proposed algorithm with the purpose of improving the ability of exploring the search space and locating the region of global optimum [33]. Also, Jadon et al used DE to modify the onlooker bee phase of artificial bee colony (ABC) algorithm [34]. DE/best/1 is adopted to accelerate the convergence speed and maintain population diversity.…”

Section: Mutation Mechanismmentioning

confidence: 99%

Dynamic Harris Hawks Optimization with Mutation Mechanism for Satellite Image Segmentation

et al. 2019

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In this paper, a novel satellite image segmentation technique based on dynamic Harris hawks optimization with a mutation mechanism (DHHO/M) is proposed. Compared with the original Harris hawks optimization (HHO), the dynamic control parameter strategy and mutation operator used in DHHO/M can avoid falling into the local optimum and efficiently enhance the search capability. To evaluate the performance of the proposed method, a series of experiments are carried out on various satellite images. Eight advanced thresholding approaches are selected for comparison. Three criteria are adopted to determine the segmentation thresholds, namely Kapur’s entropy, Tsallis entropy, and Otsu between-class variance. Furthermore, four oil pollution images are used to further assess the practicality and feasibility of the proposed method on real engineering problem. The experimental results illustrate that the DHHO/M based thresholding technique is superior to others in the following three aspects: fitness function evaluation, image segmentation effect, and statistical tests.

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Section: Mutation Mechanismmentioning

confidence: 99%

Dynamic Harris Hawks Optimization with Mutation Mechanism for Satellite Image Segmentation

et al. 2019

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“…Xiang et al [44] proposed a particle swarm like multi-elitist ABC (PS-MEABC), in which the global best solution and an elitist randomly selected from the elitist archive are used to modify the parameters of food sources. Jadon et al [45] presented a hybridization of ABC and DE algorithms called HABCDE, in which the onlooker bee phase is improved by evolutionary operators of basic DE process for faster convergence. Liang et al [27] put forward an enhanced ABC algorithm with adaptive differential operators (ABCADE) in which the differential operators are adopted to generate new solutions with an increasing probability, and the associated parameters are adaptively adjusted based on Gaussian distribution.…”

Section: Literature Reviewmentioning

confidence: 99%

Self-adaptive differential artificial bee colony algorithm for global optimization problems

Tianfield

2019

Swarm and Evolutionary Computation

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Artificial bee colony algorithm (ABC) has attracted wide attention in the recent decade. Although ABC algorithms can achieve good performance on separable problems by optimizing each variable independently, their performances on complex non-separable problems are still unsatisfactory. In this paper, through incorporating multiple differential search strategies and a self-adaptive mechanism within the framework of ABC, we propose a new ABC algorithm, called self-adaptive differential artificial bee colony (sdABC) algorithm. By means of differential search strategies, more variables will be updated each time based on the combination of mutation and crossover. Thus, sdABC has much enhanced ability for solving complex non-separable problems. Our proposed sdABC algorithm is evaluated on 28 benchmarks functions, including both common separable problems and complex non-separable CEC2015 functions. The experimental results show that sdABC can achieve much more desirable results compared with previous ABC algorithms on both separable and non-separable functions. Furthermore, the performance of our sdABC is also very competitive compared with well-established differential evolution and other meta-heuristic algorithms.

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“…In the reviewed methods, there is not a common advantage, and some of the reviewed methods consider the QoS, and some of them have high convergence speed. However, the BCO suffers from premature convergence, unbalanced exploration‐exploitation, and slow convergence speed …”

Section: Service Composition Strategiesmentioning

confidence: 99%

Nature inspired meta‐heuristic algorithms for solving the service composition problem in the cloud environments

Asghari

Navimipour

2018

Int J Communication

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Summary Many sorts of services in the cloud environments must be composited based on the user's requests to meet the requirements. Thus, the distributed services are joined to the cloud services through service composition. Also, it is known as NP‐hard problems and many researchers significantly are focused on this problem in recent years. Therefore, many different nature‐inspired meta‐heuristic techniques are proposed for solving this problem. The nature‐inspired meta‐heuristic techniques have an important role in solving the service composition problem in the cloud environments, but there is not a wide‐ranging and detailed paper about reviewing and studying the important mechanisms in this field. Therefore, this study presents a comprehensive analysis of the nature‐inspired meta‐heuristic techniques for the service composition issue in the cloud computing. The review also contains a classification of the important techniques. These classifications include Ant Colony Optimization, Bee Colony Optimization, Genetic Algorithm, Particle Swarm Optimization, Cuckoo Optimization Algorithm, Bat Algorithm, greedy algorithm, and hybrid algorithm. An important aim of this paper is to highlight the emphasis on the optimization algorithms, and the benefits to tackle the challenges are encountered in the cloud service composition. Also, this paper presents the advantages and disadvantages of the nature‐inspired meta‐heuristic algorithms for solving the service composition problem in the cloud environments. Moreover, this paper aims to provide more efficient service composition algorithms in the future. Finally, the obtained results have shown that the discussed algorithms have an important effect in solving the cloud service composition problem, and this effect has been increased in recent years.

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Hybrid Artificial Bee Colony algorithm with Differential Evolution

Cited by 162 publications

References 19 publications

Dynamic Harris Hawks Optimization with Mutation Mechanism for Satellite Image Segmentation

Dynamic Harris Hawks Optimization with Mutation Mechanism for Satellite Image Segmentation

Self-adaptive differential artificial bee colony algorithm for global optimization problems

Nature inspired meta‐heuristic algorithms for solving the service composition problem in the cloud environments

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