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Hybridizing slime mould algorithm with simulated annealing algorithm: a hybridized statistical approach for numerical

Leela Kumari Ch¹, Vikram Kumar Kamboj^1,2, S K Bath³

¹Domain of Power Systems, School of Electronics and Electrical Engineering, Lovely Professional University, Punjab, India.

Complex & Intelligent Systems

|September 26, 2022

Summary

This summary is machine-generated.

This study introduces a hybridized slime mould algorithm-simulated annealing algorithm to overcome slow convergence in optimization problems. The new method enhances parameter variation and escapes local optima, outperforming existing techniques.

Keywords:

CEC-2005 Engineering optimization Hybrid search algorithms Metaheuristics search

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Area of Science:

Computational Intelligence
Optimization Algorithms
Metaheuristics

Background:

The standard slime mould algorithm (SMA) suffers from slow convergence and poor exploitation, limiting its effectiveness in complex search spaces.
Escaping local optima and improving parameter variation are critical challenges in metaheuristic optimization.

Purpose of the Study:

To develop a novel optimization technique by hybridizing the slime mould algorithm with simulated annealing.
To enhance the convergence speed and exploitation capabilities of the slime mould algorithm.

Main Methods:

A hybridized slime mould algorithm-simulated annealing (HSMA-SA) was developed by integrating simulated annealing into the SMA framework.
The HSMA-SA was tested on benchmark functions (unimodal, multimodal, fixed-dimension) and 11 interdisciplinary engineering design problems.
Performance was evaluated against other established optimization methods.

Main Results:

The HSMA-SA demonstrated superior performance compared to existing optimization techniques.
The hybridization effectively improved parameter variation and facilitated escape from local optima.
The proposed method achieved better results on both benchmark functions and complex engineering design tasks.

Conclusions:

The hybridized slime mould algorithm-simulated annealing offers a robust and efficient solution for optimization problems.
This approach significantly enhances the capabilities of the original slime mould algorithm.
The HSMA-SA is a promising technique for addressing challenging nonlinear and nonconvex optimization tasks.