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Datasets on statistical analysis and performance evaluation of backtracking search optimisation algorithm compared

Bryar A Hassan1,2, Tarik A Rashid3

  • 1Kurdistan Institution for Strategic Studies and Scientific Research, Sulaimani, Iraq.

Data in Brief
|January 11, 2020
PubMed
Summary
This summary is machine-generated.

This study presents data comparing the backtracking search optimization algorithm (BSA) against four other evolutionary algorithms. The backtracking search optimization algorithm (BSA) demonstrated competitive statistical success across 16 benchmark problems.

Keywords:
BSA experimental dataBSA performance evaluationBacktracking search optimisation algorithmStatistical analysis

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Area of Science:

  • Computational intelligence
  • Optimization algorithms
  • Data science

Background:

  • Evolutionary optimization algorithms are widely used for complex problem-solving.
  • The backtracking search optimization algorithm (BSA) is a notable contender in this field.
  • Comparative performance data is crucial for algorithm selection.

Purpose of the Study:

  • To present data evaluating the statistical success of the backtracking search optimization algorithm (BSA).
  • To compare BSA against four other evolutionary optimization algorithms: differential evolution (DE), particle swarm optimization (PSO), artificial bee colony (ABC), and firefly algorithm (FF).
  • To provide a basis for understanding algorithm performance across diverse optimization challenges.

Main Methods:

  • Three statistical tests were conducted on BSA and four comparative algorithms.
  • Evaluation was performed on 16 benchmark optimization problems.
  • Performance was assessed considering criteria such as parameter initialization, problem dimensionality, search space, iterations, computational resources, randomization effects, and problem difficulty.

Main Results:

  • The data article details the statistical measures obtained from the comparative tests.
  • Measures include mean solution, standard deviation, best/worst solutions, execution time, and success/failure counts for each algorithm.
  • This dataset facilitates a thorough statistical analysis of algorithm performance.

Conclusions:

  • The presented data enables a comprehensive statistical evaluation of BSA against leading evolutionary algorithms.
  • The findings support informed decisions regarding the selection of optimization algorithms for specific problems.
  • This work contributes to the operational framework for advanced optimization techniques.