Convergence of Memetic Algorithms Through Neural Network Integration
Subject Areas : New technologies in distributed systems and algorithmic computing
Mohammadreza Dehghanimahoudabadi
1
*
,
Elham Dehghan tezerjani
2
1 - Department of Computer Engineering, Faculty of Engineering and Technology, Islamic Azad University, Bafq Branch, Bafq, Iran
2 - Faculty of Computer Engineering, University of Azad Islamic Bafgh, Bafgh, Iran,
Keywords: Memetic Algorithm, Neural Network, Optimization, Hybrid,
Abstract :
Memetic algorithms, which rely on population-based search and repeated evaluation of objective functions, demonstrate strong performance particularly in scenarios where objective function evaluations are computationally expensive. However, one of the major challenges associated with these algorithms is their relatively slow convergence rate and the need for a large number of generations to achieve optimal solutions.
To overcome this limitation, a hybrid algorithm is proposed that integrates a neural network within the memetic framework. In the proposed method, the neural network acts as a guiding component to steer the search process, thereby enhancing convergence dynamics and accelerating the attainment of optimal solutions.
The effectiveness of the proposed algorithm has been evaluated through a set of standard benchmark tests. Experimental results indicate a significant improvement in convergence speed and up to 88% enhancement in solution quality. These findings suggest that the hybrid approach not only substantially improves the performance of traditional memetic algorithms but also offers strong generalizability across a wide range of optimization problems.
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