A cluster-based clonal selection algorithm for optimization in dynamic environment

Zhang, Weiwei; Zhang, Weizheng; Yen, Gary G.; Jing, Honglei

doi:10.1016/j.swevo.2018.10.005

Cited by 28 publications

(12 citation statements)

References 26 publications

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“…CSA is an evolutionary algorithm, inspired by the natural phenomenon of the biological immune system, which defends the body against external microorganisms. [ 42 ] reviewed recent works by researchers implementing CSA into their proposed network to deal with constraint optimization tasks, such as pattern recognition [ 43 ], scheduling [ 44 ], fault detection [ 45 ] and dynamic optimization [ 46 ]. Mechanisms of CSA gives the inspiration of specific cells to recognize specific antigens which are later selected to proliferate.…”

Section: Introductionmentioning

confidence: 99%

Amazon Employees Resources Access Data Extraction via Clonal Selection Algorithm and Logic Mining Approach

Zamri

Mansor

Kasihmuddin

et al. 2020

Entropy

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Amazon.com Inc. seeks alternative ways to improve manual transactions system of granting employees resources access in the field of data science. The work constructs a modified Artificial Neural Network (ANN) by incorporating a Discrete Hopfield Neural Network (DHNN) and Clonal Selection Algorithm (CSA) with 3-Satisfiability (3-SAT) logic to initiate an Artificial Intelligence (AI) model that executes optimization tasks for industrial data. The selection of 3-SAT logic is vital in data mining to represent entries of Amazon Employees Resources Access (AERA) via information theory. The proposed model employs CSA to improve the learning phase of DHNN by capitalizing features of CSA such as hypermutation and cloning process. This resulting the formation of the proposed model, as an alternative machine learning model to identify factors that should be prioritized in the approval of employees resources applications. Subsequently, reverse analysis method (SATRA) is integrated into our proposed model to extract the relationship of AERA entries based on logical representation. The study will be presented by implementing simulated, benchmark and AERA data sets with multiple performance evaluation metrics. Based on the findings, the proposed model outperformed the other existing methods in AERA data extraction.

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Section: Introductionmentioning

confidence: 99%

Amazon Employees Resources Access Data Extraction via Clonal Selection Algorithm and Logic Mining Approach

Zamri

Mansor

Kasihmuddin

et al. 2020

Entropy

View full text Add to dashboard Cite

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“…Recently, Luo et al (2019) proposed a distributed multiple population framework to increase algorithm diversity for solving DOP. Zhang et al (2019b) proposed a new cluster-based clonal selection algorithm, where a max-min distance cluster method based on the fitness and Euclidean distance was used to partition the population. Vafashoar and Meybodi (2020) proposed a multipopulation DE algorithm, which is different from past heterogeneous algorithms.…”

Section: (A) Multi-populationsmentioning

confidence: 99%

A survey on evolutionary computation for complex continuous optimization

et al. 2021

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Complex continuous optimization problems widely exist nowadays due to the fast development of the economy and society. Moreover, the technologies like Internet of things, cloud computing, and big data also make optimization problems with more challenges including Many-dimensions, Many-changes, Many-optima, Many-constraints, and Many-costs. We term these as 5-M challenges that exist in large-scale optimization problems, dynamic optimization problems, multi-modal optimization problems, multi-objective optimization problems, many-objective optimization problems, constrained optimization problems, and expensive optimization problems in practical applications. The evolutionary computation (EC) algorithms are a kind of promising global optimization tools that have not only been widely applied for solving traditional optimization problems, but also have emerged booming research for solving the above-mentioned complex continuous optimization problems in recent years. In order to show how EC algorithms are promising and efficient in dealing with the 5-M complex challenges, this paper presents a comprehensive survey by proposing a novel taxonomy according to the function of the approaches, including reducing problem difficulty, increasing algorithm diversity, accelerating convergence speed, reducing running time, and extending application field. Moreover, some future research directions on using EC algorithms to solve complex continuous optimization problems are proposed and discussed. We believe that such a survey can draw attention, raise discussions, and inspire new ideas of EC research into complex continuous optimization problems and real-world applications.

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“…where g * (i) is the best found position in the ith iteration. This method is first introduced in [36] and then has been used in several DOAs [37]- [41]. A considerable flaw is that it only can detect convergence if the best found position has not improved.…”

Section: A Convergence Detectionmentioning

confidence: 99%

A Survey of Evolutionary Continuous Dynamic Optimization Over Two Decades—Part A

Yazdani

Cheng

Yazdani

et al. 2021

IEEE Trans. Evol. Computat.

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A cluster-based clonal selection algorithm for optimization in dynamic environment

Cited by 28 publications

References 26 publications

Amazon Employees Resources Access Data Extraction via Clonal Selection Algorithm and Logic Mining Approach

Amazon Employees Resources Access Data Extraction via Clonal Selection Algorithm and Logic Mining Approach

A survey on evolutionary computation for complex continuous optimization

A Survey of Evolutionary Continuous Dynamic Optimization Over Two Decades—Part A

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