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Nature-Inspired Metaheuristic Algorithms for Constraint Handling: Challenges, Issues, and Research Perspective

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Constraint Handling in Metaheuristics and Applications

Abstract

Because of getting the efficient and accurate results in the field of optimization problem solving, researchers are taking much attentiveness in heuristic and metaheuristic approaches. Through utilizing the experimentation strategy Heuristic algorithms help in generating the accurate results to every problem. The response time of metaheuristic algorithms are much higher as compared with others. Various nature-based metaheuristic algorithms are easily accessible. And because of their effective applications and high power they are being widely used in various literatures like in a field of their applications, analysis, comparison, and algorithms. But still knowing its wide sights it is also called as “black box” because some time metaheuristic algorithms perform better and sometime results are too low on are given optimization problems. Metaheuristics are said to be most efficient for solving constraints in optimization problems. Metaheuristic algorithms can be categorized over various classes for separating them between different searching patterns and describe how the algorithm copy a specific phenomenal performance in the search area, diverse classification explored. The main focus of this chapter is to get the light over various constraints handling techniques, Importance of metaheuristic algorithms in constraint handling, and metaheuristic classification approach with proper flow diagram. In this paper, we have highlighted various interesting metaheuristic Algorithms and their application areas in different field. This chapter targets to review all metaheuristics applications in different fields like healthcare, data clustering, power system problem, optimization problem, and prediction process. Further taxonomy about metaheuristic algorithms is also the part of the chapter. One of the sections in this chapter gives the Comparison of different optimization algorithms in different research fields.

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Author information

Authors and Affiliations

  1. CSE, Chandigarh University, Gharuan, Mohali, Punjab, India

    Surabhi Kaul

  2. CSE, Chandigarh Group of Colleges, Landran, Mohali, Punjab, India

    Yogesh Kumar

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  1. Surabhi Kaul
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  2. Yogesh Kumar
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Correspondence to Yogesh Kumar .

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Editors and Affiliations

  1. Symbiosis Centre for Research and Innovation, Symbiosis International University, Pune, Maharashtra, India

    Anand J. Kulkarni

  2. Artificial Intelligence Research Center, University of Veracruz, Xalapa, Veracruz, Mexico

    Efrén Mezura-Montes

  3. School of Automation, Central South University, Changsha, China

    Yong Wang

  4. Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW, Australia

    Amir H. Gandomi

  5. Monash University Malaysia, Selangor, Malaysia

    Ganesh Krishnasamy

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Kaul, S., Kumar, Y. (2021). Nature-Inspired Metaheuristic Algorithms for Constraint Handling: Challenges, Issues, and Research Perspective. In: Kulkarni, A.J., Mezura-Montes, E., Wang, Y., Gandomi, A.H., Krishnasamy, G. (eds) Constraint Handling in Metaheuristics and Applications. Springer, Singapore. https://doi.org/10.1007/978-981-33-6710-4_3

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  • DOI: https://doi.org/10.1007/978-981-33-6710-4_3

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