A Comprehensive Survey of Test Functions for Evaluating the Performance of Particle Swarm Optimization Algorithm

Abstract

Test functions play an important role in validating and comparing the performance of optimization algorithms. The test functions should have some diverse properties, which can be useful in testing of any new algorithm. The efficiency, reliability and validation of optimization algorithms can be done by using a set of standard benchmarks or test functions. For any new optimization, it is necessary to validate its performance and compare it with other existing algorithms using a good set of test functions. Optimization problems are widely used in various fields of science and technology. Sometimes such problems can be very complex. Particle Swarm Optimization is a stochastic algorithm used for solving such optimization problems. This paper transplants some of the test functions which can be used to test the performance of Particle Swarm Optimization (PSO) algorithm, in order to improve its performance and have better results. Different test functions can be used for different types of problems. These test functions have a specific range and values, which can be applied in different situations. These functions, when applied to the PSO algorithm, can give the better comparison of results. The test functions that have been the most commonly adopted to assess performance of PSO-based algorithms and details of each of them are provided, such as the search range, the position of their known optima, and other relevant properties.