Advances in Metaheuristic Optimization Algorithms

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".

Deadline for manuscript submissions: 31 January 2026 | Viewed by 533

Special Issue Editor


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Guest Editor
Electronics department, University of Guadalajara, CUCEI. Av. Revolución 1500, Guadalajara, Jal C.P 44430, Mexico
Interests: artificial intelligence algorithms

Special Issue Information

Dear Colleagues,

Metaheuristic optimization algorithms have seen significant advancements in recent decades, driven by their ability to tackle complex and diverse optimization problems across various domains. These algorithms, inspired by natural and artificial processes, have provided robust and flexible solutions for intricate computational challenges. Therefore, this Special Issue aims to highlight the latest developments, innovative methodologies, and practical applications of these algorithms.

We seek papers that explore novel metaheuristic approaches, hybrid algorithms, and their theoretical foundations. Contributions that demonstrate the application of metaheuristic optimization in real-world problems, including but not limited to engineering design, logistics, bioinformatics, finance, and artificial intelligence, are highly encouraged. Additionally, we welcome studies that compare the performance of different metaheuristic techniques, investigate parameter tuning strategies, and propose new benchmarks for algorithm evaluation.

This Special Issue aims to serve as a comprehensive resource for researchers and practitioners, fostering a deeper understanding of metaheuristic optimization and its potential to solve complex problems in various scientific and industrial fields.

Prof. Alma Rodríguez
Guest Editor

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Keywords

  • metaheuristics
  • swarm intelligence
  • evolutionary computation
  • artificial intelligence
  • bio-inspired optimization methods
  • applied optimization

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Published Papers (1 paper)

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52 pages, 2431 KiB  
Article
A Novel Bio-Inspired Optimization Algorithm Based on Mantis Shrimp Survival Tactics
by José Alfonso Sánchez Cortez, Hernán Peraza Vázquez and Adrián Fermin Peña Delgado
Mathematics 2025, 13(9), 1500; https://doi.org/10.3390/math13091500 - 1 May 2025
Viewed by 80
Abstract
This paper presents a novel meta-heuristic algorithm inspired by the visual capabilities of the mantis shrimp (Gonodactylus smithii), which can detect linearly and circularly polarized light signals to determine information regarding the polarized light source emitter. Inspired by these unique visual [...] Read more.
This paper presents a novel meta-heuristic algorithm inspired by the visual capabilities of the mantis shrimp (Gonodactylus smithii), which can detect linearly and circularly polarized light signals to determine information regarding the polarized light source emitter. Inspired by these unique visual characteristics, the Mantis Shrimp Optimization Algorithm (MShOA) mathematically covers three visual strategies based on the detected signals: random navigation foraging, strike dynamics in prey engagement, and decision-making for defense or retreat from the burrow. These strategies balance exploitation and exploration procedures for local and global search over the solution space. MShOA’s performance was tested with 20 testbench functions and compared against 14 other optimization algorithms. Additionally, it was tested on 10 real-world optimization problems taken from the IEEE CEC2020 competition. Moreover, MShOA was applied to solve three studied cases related to the optimal power flow problem in an IEEE 30-bus system. Wilcoxon and Friedman’s statistical tests were performed to demonstrate that MShOA offered competitive, efficient solutions in benchmark tests and real-world applications. Full article
(This article belongs to the Special Issue Advances in Metaheuristic Optimization Algorithms)
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