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Performance up-gradation of Symbiotic Organisms Search by Backtracking Search Algorithm.

Sukanta Nama^1,2, Apu Kumar Saha², Sushmita Sharma²

¹Department of Applied Mathematics, Maharaja Bir Bikram University, Agartala, Tripura India.

Journal of Ambient Intelligence and Humanized Computing

|April 19, 2021

Summary

This summary is machine-generated.

A new e-SOSBSA algorithm enhances optimization by combining Symbiotic Organisms Search (SOS) and Backtracking Search Algorithm (BSA). This novel approach overcomes local optima stagnation, improving efficiency and convergence for complex problems.

Keywords:

Backtracking Search Algorithm CEC2014 CEC2015 CEC2017 CEC2020 Engineering Problem Ensemble algorithm Function optimization Symbiotic Organisms Search

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Published on: December 9, 2012

Area of Science:

Computational Intelligence
Optimization Algorithms
Swarm Intelligence

Background:

Traditional Symbiotic Organisms Search (SOS) suffers from slow convergence and local optima stagnation.
Backtracking Search Algorithm (BSA) is an alternative optimization method for complex problems.

Purpose of the Study:

To address the limitations of SOS by proposing an enhanced ensemble algorithm, e-SOSBSA.
To improve the intensification and diversification capabilities of optimization algorithms.

Main Methods:

Incorporating BSA's self-adaptive mutation operator into SOS to escape local optima.
Integrating BSA's adaptive crossover operator to explore previously visited search regions effectively.
Ensemble of Symbiotic Organisms Search and Backtracking Search Algorithm (e-SOSBSA).

Main Results:

e-SOSBSA demonstrated superior performance compared to component algorithms and state-of-the-art methods across various benchmark functions (CEC2014, CEC2015, CEC2017, CEC2020).
Statistical, convergence, and diversity analyses confirmed the enhanced search capabilities of e-SOSBSA.
The algorithm proved effective in solving seven real-world engineering optimization problems.

Conclusions:

The proposed e-SOSBSA algorithm significantly overcomes the drawbacks of traditional SOS.
e-SOSBSA offers a robust and effective approach for complex optimization tasks, including engineering applications.