Evolutionary Approaches for Multi-objective Optimization and Pareto-Optimal Solution Selection in Data Analytics

Harkare, Vijay; Mangrulkar, Ramchandra; Thorat, Onkar; Jain, Sachin R.

doi:10.1007/978-981-97-0353-1_4

Springer Tracts in Nature-Inspired Computing

2024

DOI: 10.1007/978-981-97-0353-1_4

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Evolutionary Approaches for Multi-objective Optimization and Pareto-Optimal Solution Selection in Data Analytics

Vijay Harkare,

Ramchandra Mangrulkar,

Onkar Thorat

et al.

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Cited by 2 publications

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Application of a hybrid two-stage optimization framework for sustainable machining: a case study

Atif,

Junejo,

Amin

2024

Int J Adv Manuf Technol

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Application of a hybrid two-stage optimization framework for sustainable machining: a case study

Atif,

Junejo,

Amin

2024

Int J Adv Manuf Technol

View full text Add to dashboard Cite

Advancements in Optimization: Critical Analysis of Evolutionary, Swarm, and Behavior-Based Algorithms

Rashed,

Ali,

Rashid

2024

Algorithms

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The research work on optimization has witnessed significant growth in the past few years, particularly within multi- and single-objective optimization algorithm areas. This study provides a comprehensive overview and critical evaluation of a wide range of optimization algorithms from conventional methods to innovative metaheuristic techniques. The methods used for analysis include bibliometric analysis, keyword analysis, and content analysis, focusing on studies from the period 2000–2023. Databases such as IEEE Xplore, SpringerLink, and ScienceDirect were extensively utilized. Our analysis reveals that while traditional algorithms like evolutionary optimization (EO) and particle swarm optimization (PSO) remain popular, newer methods like the fitness-dependent optimizer (FDO) and learner performance-based behavior (LPBB) are gaining attraction due to their adaptability and efficiency. The main conclusion emphasizes the importance of algorithmic diversity, benchmarking standards, and performance evaluation metrics, highlighting future research paths including the exploration of hybrid algorithms, use of domain-specific knowledge, and addressing scalability issues in multi-objective optimization.

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Evolutionary Approaches for Multi-objective Optimization and Pareto-Optimal Solution Selection in Data Analytics

Cited by 2 publications

References 28 publications

Application of a hybrid two-stage optimization framework for sustainable machining: a case study

Application of a hybrid two-stage optimization framework for sustainable machining: a case study

Advancements in Optimization: Critical Analysis of Evolutionary, Swarm, and Behavior-Based Algorithms

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