Datasets on statistical analysis and performance evaluation of backtracking search optimisation algorithm compared with its counterpart algorithms

Bryar A Hassan; Tarik A Rashid

doi:10.1016/j.dib.2019.105046

Datasets on statistical analysis and performance evaluation of backtracking search optimisation algorithm compared with its counterpart algorithms

Data Brief. 2019 Dec 23:28:105046. doi: 10.1016/j.dib.2019.105046. eCollection 2020 Feb.

Authors

Bryar A Hassan^{1

2}, Tarik A Rashid³

Affiliations

¹ Kurdistan Institution for Strategic Studies and Scientific Research, Sulaimani, Iraq.
² Department of Computer Networks, Technical College of Informatics, Sulaimani Polytechnic University, Sulaimani, Iraq.
³ Computer Science and Engineering Department, University of Kurdistan Hewler, Erbil, Iraq.

Abstract

In this data article, we present the data used to evaluate the statistical success of the backtracking search optimisation algorithm (BSA) in comparison with the other four evolutionary optimisation algorithms. The data presented in this data article is related to the research article entitles 'Operational Framework for Recent Advances in Backtracking Search Optimisation Algorithm: A Systematic Review and Performance Evaluation' [1]. Three statistical tests conducted on BSA compared to differential evolution algorithm (DE), particle swarm optimisation (PSO), artificial bee colony (ABC), and firefly algorithm (FF). The tests are used to evaluate these mentioned algorithms and to determine which one could solve a specific optimisation problem concerning the statistical success of 16 benchmark problems taking several criteria into account. The criteria are initializing control parameters, dimension of the problems, their search space, and number of iterations needed to minimise a problem, the performance of the computer used to code the algorithms and their programming style, getting a balance on the effect of randomization, and the use of different type of optimisation problem in terms of hardness and their cohort. In addition, all the three tests include necessary statistical measures (Mean: mean-solution, S.D.: standard-deviation of mean-solution, Best: the best solution, Worst: the worst solution, Exec. Time: mean runtime in seconds, No. of succeeds: number of successful minimisation, and No. of Failure: number of failed minimisation).

Keywords: BSA experimental data; BSA performance evaluation; Backtracking search optimisation algorithm; Statistical analysis.