Open Access

REVIEW

Patterns in Heuristic Optimization Algorithms: A Comprehensive Analysis

Robertas Damasevicius^*

Center of Real Time Computer Systems, Kaunas University of Technology, Kaunas, 44249, Lithuania

* Corresponding Author: Robertas Damasevicius. Email:

(This article belongs to the Special Issue: Metaheuristic-Driven Optimization Algorithms: Methods and Applications)

Computers, Materials & Continua 2025, 82(2), 1493-1538. https://doi.org/10.32604/cmc.2024.057431

Received 17 August 2024; Accepted 12 December 2024; Issue published 17 February 2025

Abstract

Heuristic optimization algorithms have been widely used in solving complex optimization problems in various fields such as engineering, economics, and computer science. These algorithms are designed to find high-quality solutions efficiently by balancing exploration of the search space and exploitation of promising solutions. While heuristic optimization algorithms vary in their specific details, they often exhibit common patterns that are essential to their effectiveness. This paper aims to analyze and explore common patterns in heuristic optimization algorithms. Through a comprehensive review of the literature, we identify the patterns that are commonly observed in these algorithms, including initialization, local search, diversity maintenance, adaptation, and stochasticity. For each pattern, we describe the motivation behind it, its implementation, and its impact on the search process. To demonstrate the utility of our analysis, we identify these patterns in multiple heuristic optimization algorithms. For each case study, we analyze how the patterns are implemented in the algorithm and how they contribute to its performance. Through these case studies, we show how our analysis can be used to understand the behavior of heuristic optimization algorithms and guide the design of new algorithms. Our analysis reveals that patterns in heuristic optimization algorithms are essential to their effectiveness. By understanding and incorporating these patterns into the design of new algorithms, researchers can develop more efficient and effective optimization algorithms.

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Keywords

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