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Bandyopadhyay, S. and Saha, S. (2013) Some Single-and Multiobjective Optimization Techniques. In: Bandyopadhyay, S. and Saha, S., Eds., Unsupervised Classification: Similarity Measures, Classical and Metaheuristic Approaches, and Applications, Springer, 17-58.
https://doi.org/10.1007/978-3-642-32451-2_2

has been cited by the following article:

• TITLE: A Comprehensive Review on the Classification of Multi-Objective Optimization Techniques

  AUTHORS: Mohamd Hassan Gadallah, Abdelrahman Ali M. Ahmed

  KEYWORDS: Hybrid, Heuristic-Based Methods, Swarm-Based Methods, Priori Preference-Based Methods, Posterior Preference-Based Methods, Interactive Methods

  JOURNAL NAME: Open Journal of Optimization, Vol.14 No.4, December 1, 2025

  ABSTRACT: Multi-objective optimization remains a significant and realistic problem in engineering. A trade-off among conflicting objectives subject to equality and inequality constraints is known as the Pareto Front. The size problem is common to multi-objective optimization. Recently, several multi-objective optimization techniques have been developed; each technique has its own and different characteristics rather than others. This involves search techniques, selection methods, preferences made by the decision makers, an optimization framework, fitness evaluation, mutation or crossover methods, the nature of the problem and constraints, and the degree of complexity. Moreover, classification of problems into static and dynamic further attempts to balance some criteria such as diversity, coverage, and convergence. This paper provides an elaboration on all multi-objective optimization techniques, and shows the drawbacks addressed in the literature, which will help researchers’ understanding of the various formulations in the field.