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Some Single- and Multiobjective Optimization Techniques

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Unsupervised Classification

Abstract

Several metaheuristic techniques optimizing both single and multiple objectives are described in detail in this chapter. Mathematical formulations of the single and multiobjective optimization problems are provided. Different concepts related to multiobjective optimization are described in detail. Two popular metaheuristics, namely genetic algorithms and simulated annealing, are elaborately discussed. Several existing multiobjective evolutionary techniques (MOEAs) are described in brief. Apart from MOEAs there exist several multiobjective simulated annealing (MOSA)-based techniques. These are also described in this chapter. Finally a detailed description of a multiobjective simulated annealing-based technique, AMOSA, is provided, along with an analysis of its time complexity. Comparative results with some existing MOEA and MOSA techniques are presented for several benchmark test problems.

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Authors and Affiliations

  1. Machine Intelligence Unit, Indian Statistical Institute, Kolkata, West Bengal, India

    Sanghamitra Bandyopadhyay

  2. Dept. of Computer Science, Indian Institute of Technology, Patna, India

    Sriparna Saha

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  1. Sanghamitra Bandyopadhyay
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  2. Sriparna Saha
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Bandyopadhyay, S., Saha, S. (2013). Some Single- and Multiobjective Optimization Techniques. In: Unsupervised Classification. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32451-2_2

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  • DOI: https://doi.org/10.1007/978-3-642-32451-2_2

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