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New evolutionary operators in coloring DIMACS challenge benchmark graphs

International Journal of Information Technology (Singapore) 2022年第6期14卷 3039-3046页

作者： Marappan, Raja Bhaskaran, S. School of Computing SASTRA Deemed University Thanjavur India

Recently, graph coloring, an NP-complete problem, is an optimization problem generally applied in various applications. The solution to the graph coloring problem is found using different soft computing approaches. This research reveals new stochastic operators in solving graph coloring. In this research, the expected crossovers (c¯) and mutations (m¯) are controlled by the central conflict of the gene sequence and ∆(G), the most significant degree of the graph G. These factors played a role in minimizing the value ofg¯ , the average number of genetic generations, towards achieving stochastic convergence. The simulation results prove that these operators reduce the problem search space by reducing g¯ to bring a near-optimal coloring. The coloring for most benchmark graphs has been obtained with g¯ ϵ [2, ∆(G)/2^x], δ(G) ≥ x, δ(G) is the least degree of G. The operators use a smaller population size, N and are employed on different challenge benchmarks and the results are proved better than the state of the art methods. © 2022, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management.

关键词： Chromatic number evolutionary operators Genetic algorithm Graph coloring Optimization

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Gene Selection Using Hybrid Multi-Objective Cuckoo Search Algorithm With evolutionary operators for Cancer Microarray Data

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IEEE ACCESS 2020年 8卷 186348-186361页

作者： Othman, Mohd Shahizan Kumaran, Shamini Raja Yusuf, Lizawati Mi Univ Teknol Malaysia Sch Comp Fac Engn Skudai 81310 Malaysia

Microarray data play a huge role in recognizing a proper cancer diagnosis and classification. In most microarray data set consist of thousands of genes, but the majority number of genes are irrelevant to the diseases. An efficient algorithm for gene selection becomes important to deal with large microarray data. The main challenge is to analyze and select the relevant genes with maximum classification accuracy. Various algorithms were proposed for gene classification in previous studies, however, limited success was succeeded due to the selection of many genes in the high-dimensional microarray data. This study proposed and developed a hybrid multi-objective cuckoo search with evolutionary operators for gene selection. evolutionary operators that are used in this article were double mutation and single crossover operators. The motivation behind this research is to improve the dimensions values and explorative search abilities. Multi-objective cuckoo search with evolutionary operators employed the selection of informative genes among the high-dimensional cancer microarray data. Experiments were conducted on seven publicly available and high-dimensional cancer microarray data sets. These microarray data sets consist of approximately 2000 to 15000 genes. The results from the experiments concluded that the developed algorithm, multi-objective cuckoo search with evolutionary operators outperforms cuckoo search and multi-objective cuckoo search algorithms with a smaller number of selected significant genes.

关键词： Feature extraction Cancer Classification algorithms Optimization Gene expression Particle swarm optimization Space exploration Gene selection cancer microarray data cuckoo search multi-objective evolutionary operators

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Improving the optimization performance by an adaptable design: A dynamic selection of operators via criteria-based matrix for evolutionary algorithms

Improving the optimization performance by an adaptable desig...

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IEEE Congress on evolutionary Computation (CEC)

作者： Navarro, Mario A. Ramos-Michel, Alfonso Morales-Castaneda, Bernardo Maciel-Castillo, Oscar Aranguren, Itzel Valdivia, Arturo Oliva, Diego Mousavirad, Seyed Jalaleddin Univ Guadalajara Guadalajara Jalisco Mexico Hakim Sabzevari Univ Comp Engn Dept Sabzevar Iran

ISBN: (数字)9781665467087

ISBN: (纸本)9781665467087

The balance between exploration and exploitation is an important feature in evolutionary Algorithms (EA). The use of different operators permits to explore the search space and exploit the most prominent regions. This article introduces a dynamic operator selection method that considers different criteria at the same time. The proposed approach uses a dynamic decision matrix (DyDM) to identify which operators must be used at each iteration based on how the algorithm behaves. The DyDM considers specific information as the diversity of the algorithm to avoid stagnation, the actual iteration to work accordingly, and the fitness to direct the search. The proposed approach is called Dynamic Decision Matrix Optimizer (DyDMO) and it has been compared with different well-known algorithms tested on the CEC 2017 benchmark functions. The comparative analysis and non-parametric statistical tests validate how DyDMO improves the quality of the solutions and is more stable than its competitors.

关键词： Decision Matrix Operator selection Diversity evolutionary operators

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Does the Field of Nature-Inspired Computing Contribute to Achieving Lifelike Features?

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ARTIFICIAL LIFE 2023年第4期29卷 487-511页

作者： Tzanetos, Alexandros Univ Sherbrooke Dept Elect Engn & Comp Engn Multiobject Optimizat Res Lab Sherbrooke PQ Canada

The main idea behind artificial intelligence was simple: what if we study living systems to develop new, practical computing systems that possess "lifelike" properties? And that's exactly how evolutionary computing emerged. Researchers came up with ideas inspired by the principles of evolution to develop intelligent methods to tackle hard problems. The efficacy of these methods made researchers seek inspiration in living organisms and systems and extend the evolutionary concept to other nature-inspired ideas. In recent years, nature-inspired computing has exhibited an exponential increase in the number of algorithms that are presented each year. Authors claim that they are inspired by a behavior found in nature to come up with a lifelike algorithm. However, the mathematical background does not match the behavior in the majority of these cases. Thus the question is, do all nature-inspired algorithms remain lifelike? Also, are there any ideas included that contribute to computing? This study aims to (a) present some nature-inspired methods that contribute to achieving lifelike features of computing systems and (b) discuss if there is any need for new lifelike features.

关键词： Nature-inspired algorithms nature-inspired computing evolutionary operators nature-inspired mechanisms lifelike features

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Memory, evolutionary operator, and local search based improved Grey Wolf Optimizer with linear population size reduction technique

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KNOWLEDGE-BASED SYSTEMS 2023年第1期264卷

作者： Ahmed, Rasel Rangaiah, Gade Pandu Mahadzir, Shuhaimi Mirjalili, Seyedali Hassan, Mohamed H. Kamel, Salah Univ Teknol PETRONAS Dept Chem Engn Seri Iskandar Malaysia Virginia Polytech Inst & State Univ Dept Chem Engn Blacksburg VA 24060 USA Natl Univ Singapore Dept Chem & Biomol Engn Engn Dr 4 Singapore 117576 Singapore Vellore Inst Technol Sch Chem Engn Vellore 632014 India Univ Teknol PETRONAS Inst Autonomous Syst Ctr Syst Engn Seri Iskandar Malaysia Torrens Univ Australia Ctr Artificial Intelligence Res & Optimizat Brisbane Australia Yonsei Univ Yonsei Frontier Lab Seoul South Korea Aswan Univ Fac Engn Dept Elect Engn Aswan 81542 Egypt Obuda Univ Univ Res Innovat Ctr H-1034 Budapest Hungary

Optimization of multi-modal functions is challenging even for evolutionary and swarm-based algorithms as it requires an efficient exploration for finding the promising region of the search space, and effective exploitation to precisely find the global optimum. Grey Wolf Optimizer (GWO) is a recently developed metaheuristic algorithm that is inspired by nature with a relatively small number of parameters for tuning. However, GWO and most of its variants may suffer from the lack of population diversity, premature convergence, and the inability to preserve a good balance between exploratory and exploitative behaviors. To address these limitations, this work proposes a new variant of GWO incorporating memory, evolutionary operators, and a stochastic local search technique. It further integrates Linear Population Size Reduction (LPSR) technique. The proposed algorithm is comprehensively tested on 23 numerical benchmark functions, high dimensional benchmark functions, 13 engineering case studies, four data classifications, and three function approximation problems. The benchmark functions are mostly taken from the CEC 2005 and CEC 2010 special sessions, and they include rotated, shifted functions. The engineering case studies are from the CEC 2020 real-world non-convex constrained optimization problems. The performance of the proposed GWO is compared with popular metaheuristics, namely, particle swarm optimization (PSO), gravitational search algorithm (GSA), slap swarm algorithm (SSA), differential evolution (DE), self-adaptive differential evolution (SADE), basic GWO and its three recently improved variants. Statistical analysis and Friedman tests have been conducted to thoroughly compare their performance. The obtained results demonstrate that the proposed GWO outperforms the algorithms compared for the benchmark functions and engineering case studies tested. (c) 2023 Elsevier B.V. All rights reserved.

关键词： Metaheuristics Swarm intelligence Grey Wolf Optimizer Memory evolutionary operators Stochastic local search Linear population size reduction Optimization Algorithm

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Improving Group Search Optimization for Automatic Data Clustering Using Merge and Split operators 11th

Improving Group Search Optimization for Automatic Data Clust...

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11th Brazilian Conference on Intelligent Systems (BRACIS)

作者： Pacifico, Luciano D. S. Ludermir, Teresa B. Univ Fed Rural Pernambuco Dept Comp DC Recife PE Brazil Univ Fed Pernambuco Ctr Informat Recife PE Brazil

ISBN: (纸本)9783031216855;9783031216862

The amount of digital data produced daily has increased considerably in the last years. The need for fast and reliable information in real-world applications demands ever more precise algorithms and Data Mining tools, once most of the systems in our daily lives are executed in real-time. Data clustering is one of the most important and primitive activities in Unsupervised Machine Learning, consisting in a fundamental mechanism for exploratory data analysis. Given the complexity of data clustering task, standard clustering methods, such as the partitional algorithms, are easily trapped in local optima solutions, due to their lack of good global searching operators. In this work, three improved Group Search Optimization-based approaches are proposed, based on merge and split heuristics, in the context of Automatic Clustering Analysis: MGSO, SGSO and MSGSO. Group Search Optimization (GSO) is a natural-inspired meta-heuristic, known for its good global search abilities, and mechanisms to escape from local optima points from the problem space. The proposed models attempt to perform both cluster optimization and the determination of the best number of clusters for each dataset, overcoming the limitations of traditional partitional clustering algorithms. The proposed GSO-based models are evaluated through a testing bed composed of nine real-world problems, and compared to six state-of-the-art partitional automatic clustering approaches, include standard GSO. The experimental evaluation has been performed considering five clustering metrics, and both empirical and statistical analysis. The results showed that the proposed MGSO, SGSO and MSGSO algorithms are very promising and reliable while tackling clustering problems.

关键词： Group search optimization Automatic data clustering evolutionary operators Merge and split heuristics Machine learning

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Course timetabling using evolutionary operators

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APPLIED SOFT COMPUTING 2013年第5期13卷 2504-2514页

作者： Qaurooni, Danial Akbarzadeh-T, Mohammad-R. Amirkabir Univ Technol Dept Comp Sci Tehran Iran Ferdowsi Univ Mashhad Ctr Excellence Soft Comp & Intelligent Informat P Mashhad Iran

Timetabling is the problem of scheduling a set of events while satisfying various constraints. In this paper, we develop and study the performance of an evolutionary algorithm, designed to solve a specific variant of the timetabling problem. Our aim here is twofold: to develop a competitive algorithm, but more importantly, to investigate the applicability of evolutionary operators to timetabling. To this end, the introduced algorithm is tested using a benchmark set. Comparison with other algorithms shows that it achieves better results in some, but not all instances, signifying strong and weak points. To further the study, more comprehensive tests are performed in connection with another evolutionary algorithm that uses strictly group-based operators. Our analysis of the empirical results leads us to question single-level selection, proposing, in its place, a multi-level alternative. (C) 2012 Elsevier B. V. All rights reserved.

关键词： Combinatorial optimization Scheduling and timetabling evolutionary operators Memetic algorithms Grouping genetic algorithms

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Hybrid many-objective particle swarm optimization algorithm for green coal production problem

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INFORMATION SCIENCES 2020年第0期518卷 256-271页

作者： Cui, Zhihua Zhang, Jiangjiang Wu, Di Cai, Xingjuan Wang, Hui Zhang, Wensheng Chen, Jinjun Taiyuan Univ Sci & Technol Complex Syst & Computat Intelligence Lab Taiyuan 030024 Peoples R China Nanchang Inst Technol Sch Informat Engn Nanchang 330099 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Intelligent Control & Management Co Beijing 100190 Peoples R China Swinburne Univ Technol Dept Comp Sci & Software Engn Melbourne Vic 3000 Australia

The key aspect in coal production is realizing safe and efficient mining to maximize the utilization of the resources. A requirement for sustainable economic development is realizing green coal production, which is influenced by factors of coal economic, energy, ecological, coal gangue economic and social benefits. To balance these factors, this paper proposes a many-objective optimization model with five objectives for green coal production. Furthermore, a hybrid many-objective particle swarm optimization (HMaPSO) algorithm is designed to solve the established model. A new offspring of the alternative pool is generated by employing different evolutionary operators. The environmental selection mechanism is adopted to select and store the excellent solutions. Two sets of experiments are performed to verify the effectiveness of the proposed approach: First, the HMaPSO algorithm is tested on the DTLZ functions, and its performance is compared with that of several widely used many-objective algorithms. Second, the HMaPSO algorithm is applied to solve the many-objective green coal production optimization model. The computational results demonstrate the effectiveness of the proposed approach, and the simulation results prove that the designed approach can provide promising choices for decision makers in regional planning. (C) 2020 Elsevier Inc. All rights reserved.

关键词： Coal production Many-objective optimization problems evolutionary operators Particle swarm optimization (PSO)

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Hybrid multi-objective evolutionary algorithm based on Search Manager framework for big data optimization problems

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APPLIED SOFT COMPUTING 2020年 87卷 105991-000页

作者： Abdi, Yousef Feizi-Derakhshi, Mohammad-Reza Univ Tabriz Fac Elect & Comp Engn Computerized Intelligence Syst Lab Tabriz Iran

Big Data optimization (Big-Opt) refers to optimization problems which require to manage the properties of big data analytics. In the present paper, the Search Manager (SM), a recently proposed framework for hybridizing metaheuristics to improve the performance of optimization algorithms, is extended for multi-objective problems (MOSM), and then five configurations of it by combination of different search strategies are proposed to solve the EEG signal analysis problem which is a member of the big data optimization problems class. Experimental results demonstrate that the proposed configurations of MOSM are efficient in this kind of problems. The configurations are also compared with NSGA-III with uniform crossover and adaptive mutation operators (NSGA-III UCAM), which is a recently proposed method for Big-Opt problems. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Big Data optimization Hybrid multi-objective evolutionary algorithm Search Manager framework evolutionary operators

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Whale optimization approaches for wrapper feature selection

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APPLIED SOFT COMPUTING 2018年 62卷 441-453页

作者： Mafarja, Majdi Mirjalili, Seyedali Birzeit Univ Dept Comp Sci POB 14 West Bank Palestine Griffith Univ Inst Integrated & Intelligent Syst Nathan Campus Brisbane Qld 4111 Australia

Classification accuracy highly dependents on the nature of the features in a dataset which may contain irrelevant or redundant data. The main aim of feature selection is to eliminate these types of features to enhance the classification accuracy. The wrapper feature selection model works on the feature set to reduce the number of features and improve the classification accuracy simultaneously. In this work, a new wrapper feature selection approach is proposed based on Whale Optimization Algorithm (WOA). WOA is a newly proposed algorithm that has not been systematically applied to feature selection problems yet. Two binary variants of the WOA algorithm are proposed to search the optimal feature subsets for classification purposes. In the first one, we aim to study the influence of using the Tournament and Roulette Wheel selection mechanisms instead of using a random operator in the searching process. In the second approach, crossover and mutation operators are used to enhance the exploitation of the WOA algorithm. The proposed methods are tested on standard benchmark datasets and then compared to three algorithms such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), the Ant Lion Optimizer (ALO), and five standard filter feature selection methods. The paper also considers an extensive study of the parameter setting for the proposed technique. The results show the efficiency of the proposed approaches in searching for the optimal feature subsets. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Feature selection Optimization Whale optimization algorithm Classification WOA Crossover Mutation Selection evolutionary operators

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