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Related Experiment Video

Updated: Oct 4, 2025

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

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A Bio-Inspired Multi-Population-Based Adaptive Backtracking Search Algorithm.

Sukanta Nama^1,2, Apu Kumar Saha²

¹Department of Mathematics, MBB University, Agartala, Tripura India.

Cognitive Computation

|February 7, 2022

Summary

This summary is machine-generated.

An improved Backtracking Search Algorithm (ImBSA) enhances global optimization using multi-population and adaptive parameters. This nature-based optimization technique shows superior performance on benchmark and engineering problems.

Keywords:

Backtracking search algorithm CEC2014 CEC2017 Multi-population strategy Mutation strategies

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Area of Science:

Computational Intelligence
Optimization Algorithms
Nature-Inspired Computing

Background:

Backtracking Search Algorithm (BSA) is a widely used nature-based optimization technique for global optimization.
Existing BSA variants require further improvements in performance and adaptability.

Purpose of the Study:

To introduce an improved Backtracking Search Algorithm (ImBSA).
To enhance the performance of BSA through a multi-population approach and adaptive parameter control.
To integrate novel mutation strategies for better exploration and exploitation.

Main Methods:

Developed ImBSA incorporating a multi-population strategy.
Implemented adaptive control parameter updates for all mutation strategies.
Introduced a new mutation strategy to boost algorithm performance.
Conducted extensive experiments on CEC2014 and CEC2017 benchmark functions.

Main Results:

ImBSA demonstrated superior performance compared to state-of-the-art algorithms, including DE and PSO variants.
Statistical analysis using the Friedman rank test confirmed ImBSA's efficiency.
Validated ImBSA's effectiveness on six real-world engineering design problems.

Conclusions:

The proposed ImBSA significantly outperforms existing algorithms on complex optimization tasks.
Adaptive parameter control and multi-population strategies are key to ImBSA's enhanced capabilities.
ImBSA offers a robust and efficient solution for global optimization challenges.