[Paper Review] A survey on pioneering metaheuristic algorithms between 2019 and 2024

Tansel Dökeroğlu, Deniz Cantürk+1 more|ArXiv.org|Dec 28, 2024

Metaheuristic Optimization Algorithms Research4 citations

TL;DR

This paper presents a comprehensive review of 158 metaheuristic algorithms developed between 2019 and 2024, identifying 23 as the most influential based on citation impact, problem diversity, code availability, and robustness to stagnation. It evaluates their mechanisms, applications, and usability, offering a critical benchmark for researchers selecting algorithms for complex optimization challenges.

ABSTRACT

This review examines over 150 new metaheuristics of the last six years (between 2019 and 2024), underscoring their profound influence and performance. Over the past three decades, more than 500 new metaheuristic algorithms have been proposed, with no slowdown in sight. An overwhelming abundance that complicates the process of selecting and assessing the most effective solutions for complex optimization challenges. Our evaluation centers on pivotal criteria, including annual citation metrics, the breadth of the addressed problem types, source code availability, user friendly parameter configurations, innovative mechanisms and operators, and approaches designed to mitigate traditional metaheuristic issues such as stagnation and premature convergence. We further explore recent high impact applications of the past six years' most influential 23 metahueristic algorithms, shedding light on their advantages and limitations, while identifying challenges and potential avenues for future research.

Motivation & Objective

• To address the growing challenge of selecting effective metaheuristic algorithms from over 150 new proposals between 2019 and 2024.
• To identify and evaluate the most impactful metaheuristic algorithms based on multiple criteria including citation frequency, problem diversity, and code availability.
• To analyze the strengths, limitations, and innovative mechanisms of leading algorithms to guide researchers and practitioners in algorithm selection.
• To highlight high-impact applications of the top 23 algorithms in real-world optimization problems across diverse domains.
• To identify open challenges and future research directions in metaheuristic design and application.

Proposed method

• Conducted a systematic review of 158 newly proposed metaheuristic algorithms from 2019 to 2024 using academic databases and citation tracking.
• Evaluated algorithms across six key criteria: annual citation count, range of problem types addressed, source code availability, parameter tuning ease, novelty of mechanisms, and resilience to stagnation and premature convergence.
• Selected 23 top-performing algorithms based on cumulative performance across the evaluation criteria, with citation counts as a primary filter.
• Performed in-depth comparative analysis of the 23 selected algorithms, focusing on unique operators, search strategies, and convergence behavior.
• Collected and synthesized state-of-the-art applications of the top 23 algorithms from 2019–2024 to demonstrate real-world impact.
• Identified trends, limitations, and research gaps to inform future algorithm development and application.

Figure 1 : The query results on Google Scholar and Web of Science (WOS) with the keywords ”metaheuristic” for the last 15 years (2011 and 2024).

Experimental results

Research questions

• RQ1 Which metaheuristic algorithms introduced between 2019 and 2024 have demonstrated the highest influence and sustained research impact?
• RQ2 How do the top-performing metaheuristics differ in terms of mechanism design, parameter sensitivity, and robustness to optimization pitfalls like stagnation?
• RQ3 What is the relationship between algorithm novelty, code availability, and adoption rate in the research community?
• RQ4 What are the most prominent application domains for the most influential metaheuristics between 2019 and 2024?
• RQ5 What common challenges and limitations persist across leading metaheuristic designs, and what future research directions are most promising?

Key findings

• The Harris Hawks Optimization algorithm received 11,300 citations by November 2024, making it the most cited metaheuristic introduced between 2019 and 2024.
• The Gradient-based Optimizer (2020) and Slime Mould Algorithm (2020) each achieved over 5,000 citations, indicating strong adoption and impact across diverse optimization problems.
• Algorithms such as the Dung Beetle Optimizer (2023) and Chaos Game Optimization (2024) show rising influence despite recent introduction, with over 900 and 760 citations respectively by late 2024.
• Only 23 out of 158 reviewed algorithms met the high-impact threshold across all evaluation criteria, highlighting the need for rigorous benchmarking in metaheuristic research.
• A significant number of high-citation algorithms (e.g., Marine Predators, Equilibrium Optimizer) were published in 2020, suggesting a peak in innovation during that year.
• Algorithms with open-source code and user-friendly parameter settings were consistently ranked higher in usability and adoption, emphasizing the importance of accessibility in algorithm design.

Figure 2 : The steps of the HHO metaheuristic according to the energy level (E), $q$ and $r$ values.