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New measures for comparing optimization algorithms on dynamic optimization problems

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Abstract

Dynamic optimization problems have emerged as an important field of research during the last two decades, since many real-world optimization problems are changing over time. These problems need fast and accurate algorithms, not only to locate the optimum in a limited amount of time but also track its trajectories as close as possible. Although lots of research efforts have been given in developing dynamic benchmark generator/problems and proposing algorithms to solve these problems, the role of numerical performance measurements have been barely considered in the literature. Several performance criteria have been already proposed to evaluate the performance of algorithms. However, because they only take confined aspects of the algorithms into consideration, they do not provide enough information about the effectiveness of each algorithm. In this paper, at first we review the existing performance measures and then we present a set of two measures as a framework for comparing algorithms in dynamic environments, named fitness adaptation speed and alpha–accuracy. A comparative study is then conducted among different state-of-the-art algorithms on moving peaks benchmark via proposed metrics, along with several other performance measures, to demonstrate the relative advantages of the introduced measures. We hope that the collected knowledge in this paper opens a door toward a more comprehensive comparison among algorithms for dynamic optimization problems.

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Acknowledgement

The authors are grateful to Dr. A.B. Hashemi for letting us use the source code of HmSO.

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Authors and Affiliations

  1. Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Javidan Kazemi Kordestani

  2. Soft Computing Laboratory, Computer Engineering and Information Technology, Department, Amirkabir University of Technology (Tehran Polytechnic), 424 Hafez Ave., Tehran, Iran

    Alireza Rezvanian & Mohammad Reza Meybodi

  3. Department of Computer Engineering and Information Technology, Hamedan University of Technology, Hamedan, Iran

    Alireza Rezvanian

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  1. Javidan Kazemi Kordestani
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  2. Alireza Rezvanian
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Correspondence to Alireza Rezvanian.

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Kordestani, J.K., Rezvanian, A. & Meybodi, M.R. New measures for comparing optimization algorithms on dynamic optimization problems. Nat Comput 18, 705–720 (2019). https://doi.org/10.1007/s11047-016-9596-8

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