An efficient hybrid clustering method based on improved cuckoo optimization and modified particle swarm optimization algorithms

Bouyer, Asgarali; Hatamlou, Abdolreza

doi:10.1016/j.asoc.2018.03.011

Cited by 89 publications

(41 citation statements)

References 41 publications

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“…Algorithms include particle such as PSO, ACO, Gray wolf optimization (GWO), artificial bee colony (ABC), owl optimization algorithm (OOA), Falcon optimization algorithm (FOA), cuckoo search algorithm (CSA), and firefly algorithm (FA). Many researchers around the world have been benefited from the diversity in swarm-based algorithms, which are applied to solve complex optimization problems in various fields such as test scheduling problems, 23,24 engineering optimization problems, 10,11,[25][26][27][28] heat exchangers problems, [29][30][31] neural network parameter optimization, 32,33 health-care, 34,35 real-time object tracking, 36,37 protein detection, 38,39 task scheduling in cloud computing, 40,41 and clustering for wireless sensor networks. 42,43 The third category of algorithms that use physical or chemical systems, typically simulate physical phenomena occurring in nature like Newton's gravitational law, quantum mechanics, and universe theory.…”

Section: Literature Reviewmentioning

confidence: 99%

A new competitive multiverse optimization technique for solving single‐objective and multiobjective problems

Benmessahel

Xie

Chellal

2020

Engineering Reports

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The development of useful algorithms for solving global optimization problems has recently drawing the research community's attention. A number of optimization algorithms have been suggested, which mimic a particular biological process or imitate natural evolution. In this work, a novel population‐based optimization technique is proposed, the so‐called competitive multiverse optimizer (CMVO) for solving global optimization problems. This novel method is fundamentally inspired by the multiverse optimizer algorithm (MVO) but with a different framework. Our basic idea is to inject a pairwise competition mechanism between universes and adopts a novel update strategy that make universes learn from the winner. In CMVO, the mechanism of updating universes is not confined to the optimal universe but rather uses a bicompetitive scheme at each generation in which the universe that loses the competition learns from the winner, unlike in MVO in which all universes learn from the optimal universe. The main idea of this work is to raise the exploration rate for the search space by performing pairwise competition and improve the exploitation ability through learning from the winner. This work also presents a multi‐objective version of CMVO called MOCMVO with a simple structure to show the rapid convergence toward the Pareto front. The performance of our proposed method in single‐objective optimization is demonstrated on a large number of mathematical problems of standard benchmark. The results obtained confirm the superior overall performance of CMVO in terms of the quality of obtained solutions, computational efficiency, and convergence speed relative to many of the state‐of‐the‐art metaheuristic algorithms. In multiobjective optimization, the proposed MOCMVO is evaluated and compared to other multiobjective algorithms using 10 multiobjective benchmarks involving five unconstrained, three constrained, and two engineering design problems. The experimental results of the proposed method in multiobjective optimization demonstrate the competitiveness of the proposed method in term of quantitative and qualitative measures.

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Section: Literature Reviewmentioning

confidence: 99%

A new competitive multiverse optimization technique for solving single‐objective and multiobjective problems

Benmessahel

Xie

Chellal

2020

Engineering Reports

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“…One of the well-known techniques to handle the clustering problem is to convert the clustering problem into an optimization problem. Then, the clustering problem can be solved by any optimization algorithm [63]. That is, clustering itself can be stated as an optimization problem, so it can be solved by the SIO algorithms [54].…”

Section: Comparison Of Clustering Optimization With Six Siomentioning

confidence: 99%

“…That is, clustering itself can be stated as an optimization problem, so it can be solved by the SIO algorithms [54]. Many scholars have been devoted to solving clustering problem using SIO techniques [62][63][64][65][66][67][68][69][70].…”

Section: Comparison Of Clustering Optimization With Six Siomentioning

confidence: 99%

Improved Monarch Butterfly Optimization Algorithm Based on Opposition‐Based Learning and Random Local Perturbation

Sun

Chen

et al. 2019

Complexity

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Many optimization problems have become increasingly complex, which promotes researches on the improvement of different optimization algorithms. The monarch butterfly optimization (MBO) algorithm has proven to be an effective tool to solve various kinds of optimization problems. However, in the basic MBO algorithm, the search strategy easily falls into local optima, causing premature convergence and poor performance on many complex optimization problems. To solve the issues, this paper develops a novel MBO algorithm based on opposition-based learning (OBL) and random local perturbation (RLP). Firstly, the OBL method is introduced to generate the opposition-based population coming from the original population. By comparing the opposition-based population with the original population, the better individuals are selected and pass to the next generation, and then this process can efficiently prevent the MBO from falling into a local optimum. Secondly, a new RLP is defined and introduced to improve the migration operator. This operation shares the information of excellent individuals and is helpful for guiding some poor individuals toward the optimal solution. A greedy strategy is employed to replace the elitist strategy to eliminate setting the elitist parameter in the basic MBO, and it can reduce a sorting operation and enhance the computational efficiency. Finally, an OBL and RLP-based improved MBO (OPMBO) algorithm with its complexity analysis is developed, following on which many experiments on a series of different dimensional benchmark functions are performed and the OPMBO is applied to clustering optimization on several public data sets. Experimental results demonstrate that the proposed algorithm can achieve the great optimization performance compared with a few state-of-the-art algorithms in most of the test cases.

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“…However, some algorithms were unable to find robust and effective results across many datasets [10]. This may occur due to an inefficient balance between exploitation and exploration that may lead to stagnation or premature convergence [14]. Some recent studies have suggested hybridizing a local search and a global search to obtain a good balance.…”

Section: Introductionmentioning

confidence: 99%

Solving text clustering problem using a memetic differential evolution algorithm

et al. 2020

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The text clustering is considered as one of the most effective text document analysis methods, which is applied to cluster documents as a consequence of the expanded big data and online information. Based on the review of the related work of the text clustering algorithms, these algorithms achieved reasonable clustering results for some datasets, while they failed on a wide variety of benchmark datasets. Furthermore, the performance of these algorithms was not robust due to the inefficient balance between the exploitation and exploration capabilities of the clustering algorithm. Accordingly, this research proposes a Memetic Differential Evolution algorithm (MDETC) to solve the text clustering problem, which aims to address the effect of the hybridization between the differential evolution (DE) mutation strategy with the memetic algorithm (MA). This hybridization intends to enhance the quality of text clustering and improve the exploitation and exploration capabilities of the algorithm. Our experimental results based on six standard text clustering benchmark datasets (i.e. the Laboratory of Computational Intelligence (LABIC)) have shown that the MDETC algorithm outperformed other compared clustering algorithms based on AUC metric, F-measure, and the statistical analysis. Furthermore, the MDETC is compared with the state of art text clustering algorithms and obtained almost the best results for the standard benchmark datasets.

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An efficient hybrid clustering method based on improved cuckoo optimization and modified particle swarm optimization algorithms

Cited by 89 publications

References 41 publications

A new competitive multiverse optimization technique for solving single‐objective and multiobjective problems

A new competitive multiverse optimization technique for solving single‐objective and multiobjective problems

Improved Monarch Butterfly Optimization Algorithm Based on Opposition‐Based Learning and Random Local Perturbation

Solving text clustering problem using a memetic differential evolution algorithm

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