Performance assessment of the metaheuristic optimization algorithms: an exhaustive review
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Performance assessment of the metaheuristic optimization algorithms: an exhaustive review A. Hanif Halim1 · I. Ismail1 · Swagatam Das2
© Springer Nature B.V. 2020
Abstract The simulation-driven metaheuristic algorithms have been successful in solving numerous problems compared to their deterministic counterparts. Despite this advantage, the stochastic nature of such algorithms resulted in a spectrum of solutions by a certain number of trials that may lead to the uncertainty of quality solutions. Therefore, it is of utmost importance to use a correct tool for measuring the performance of the diverse set of metaheuristic algorithms to derive an appropriate judgment on the superiority of the algorithms and also to validate the claims raised by researchers for their specific objectives. The performance of a randomized metaheuristic algorithm can be divided into efficiency and effectiveness measures. The efficiency relates to the algorithm’s speed of finding accurate solutions, convergence, and computation. On the other hand, effectiveness relates to the algorithm’s capability of finding quality solutions. Both scopes are crucial for continuous and discrete problems either in single- or multi-objectives. Each problem type has different formulation and methods of measurement within the scope of efficiency and effectiveness performance. One of the most decisive verdicts for the effectiveness measure is the statistical analysis that depends on the data distribution and appropriate tool for correct judgments. Keywords Metaheuristics · Population-based optimization · Performance metric · Performance indicator · Single and multi-objective optimization · Continuous optimization · Discrete optimization
* Swagatam Das [email protected] A. Hanif Halim [email protected] I. Ismail [email protected] 1
Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, Tronoh, Perak, Malaysia
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Electronics and Communication Science Unit, Indian Statistical Institute, 203 B. T. Road, Kolkata, India
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A. H. Halim et al.
1 Introduction The family of stochastic search and optimization algorithms has a unique characteristic of randomness, where an algorithm executes different paths towards the best solution by the same input. This attributed the applicability of the algorithms to a wide range of optimization problems. The stochastic algorithms can be further divided into two categories: heuristic and metaheuristic algorithms. Both methods are based on the same concept, which is to find the solution by some kind of guided trial and error (Yang 2010). Heuristics are mostly problem-dependent and for various problems, different heuristics can be defined. A metaheuristic method, on the other hand, makes almost no prior assumption about the problem, can integrate several heuristics inside, and is usually described in terms of a set (commonly known as a population) of candidate solutions to the problem. Thus, metaheuristics can be applied to a wide range of problems that they treat as blackboxe
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