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Journal articles on the topic 'Nature inspired algorithms metaheuristics'

Author: Grafiati

Published: 5 June 2025

Last updated: 1 August 2025

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1

Rahman, Md Ashikur, Rajalingam Sokkalingam, Mahmod Othman, Kallol Biswas, Lazim Abdullah, and Evizal Abdul Kadir. "Nature-Inspired Metaheuristic Techniques for Combinatorial Optimization Problems: Overview and Recent Advances." Mathematics 9, no. 20 (2021): 2633. http://dx.doi.org/10.3390/math9202633.

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Combinatorial optimization problems are often considered NP-hard problems in the field of decision science and the industrial revolution. As a successful transformation to tackle complex dimensional problems, metaheuristic algorithms have been implemented in a wide area of combinatorial optimization problems. Metaheuristic algorithms have been evolved and modified with respect to the problem nature since it was recommended for the first time. As there is a growing interest in incorporating necessary methods to develop metaheuristics, there is a need to rediscover the recent advancement of meta

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Rajendran, Shankar, Ganesh N., Robert Čep, Narayanan R. C., Subham Pal, and Kanak Kalita. "A Conceptual Comparison of Six Nature-Inspired Metaheuristic Algorithms in Process Optimization." Processes 10, no. 2 (2022): 197. http://dx.doi.org/10.3390/pr10020197.

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In recent years, several high-performance nature-inspired metaheuristic algorithms have been proposed. It is important to study and compare the convergence, computational burden and statistical significance of these metaheuristics to aid future developments. This study focuses on six recent metaheuristics, namely, ant lion optimization (ALO), arithmetic optimization algorithm (AOA), dragonfly algorithm (DA), grey wolf optimizer (GWO), salp swarm algorithm (SSA) and whale optimization algorithm (WOA). Optimization of an industrial machining application is tackled in this paper. The optimal mach

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Ouadfel, Salima, and Souham Meshoul. "Nature-Inspired Metaheuristics for Automatic Multilevel Image Thresholding." International Journal of Applied Metaheuristic Computing 5, no. 4 (2014): 47–69. http://dx.doi.org/10.4018/ijamc.2014100103.

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Thresholding is one of the most used methods of image segmentation. It aims to identify the different regions in an image according to a number of thresholds in order to discriminate objects in a scene from background as well to distinguish objects from each other. A great number of thresholding methods have been proposed in the literature; however, most of them require the number of thresholds to be specified in advance. In this paper, three nature-inspired metaheuristics namely Artificial Bee Colony, Cuckoo Search and Bat algorithms have been adapted for the automatic multilevel thresholding

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Almufti, Saman. "Vibrating Particles System Algorithm: Overview, Modifications and Applications." ICONTECH INTERNATIONAL JOURNAL 6, no. 3 (2022): 1–11. http://dx.doi.org/10.46291/icontechvol6iss3pp1-11.

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Real-world problems are difficult enough to encourages academics to develop innovative, effective problem-solving methods. Generally, metaheuristics algorithms that are inspired by nature, biology, and physics have a flexibility and capacity to adapt to different situations, metaheuristics based on evolutionary computation algorithms have been widely employed to solve complicated, constrained/unconstrained, single/multiple objective, and Non-deterministic polynomial hard (NP-Hard) optimization problems. This paper describes Vibrating Particles System (VPS) as a Physics Based metaheuristi

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Bacanin, Nebojsa, and Milan Tuba. "Firefly Algorithm for Cardinality Constrained Mean-Variance Portfolio Optimization Problem with Entropy Diversity Constraint." Scientific World Journal 2014 (2014): 1–16. http://dx.doi.org/10.1155/2014/721521.

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Portfolio optimization (selection) problem is an important and hard optimization problem that, with the addition of necessary realistic constraints, becomes computationally intractable. Nature-inspired metaheuristics are appropriate for solving such problems; however, literature review shows that there are very few applications of nature-inspired metaheuristics to portfolio optimization problem. This is especially true for swarm intelligence algorithms which represent the newer branch of nature-inspired algorithms. No application of any swarm intelligence metaheuristics to cardinality constrai

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Li, Hongwei, Yuvraj Gajpal, Chirag Surti, Dongliang Cai, and Amit Kumar Bhardwaj. "Nature-Inspired Metaheuristics for Two-Agent Scheduling with Due Date and Release Time." Complexity 2020 (December 29, 2020): 1–13. http://dx.doi.org/10.1155/2020/1385049.

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This paper delves into a two-agent scheduling problem in which two agents are competing for a single resource. Each agent has a set of jobs to be processed by a single machine. The processing time, release time, weight, and the due dates of each job are known in advance. Both agents have their objectives, which are conflicting in nature. The first agent tries to minimize the total completion time, while the second agent tries to minimize the number of tardy jobs. The two agents’ scheduling problem, an NP-hard problem, has a wide variety of applications ranging from the manufacturing industry t

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Kaleche, Rachid, Zakaria Bendaoud, and Karim Bouamrane. "Bio-Inspired Metaheuristics." International Journal of Organizational and Collective Intelligence 10, no. 4 (2020): 1–18. http://dx.doi.org/10.4018/ijoci.2020100101.

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In real life, problems becoming more complicated, among them NP-Hard problems. To resolve them, two families of methods exist, exact and approximate methods. When exact methods provide the optimal solution in an unacceptable amount of time, the approximate ones provide good solutions in a reasonable amount of time. Approximate methods are two kinds, heuristics and metaheuristics. The first ones are problem specific, while metaheuristics are independent from problems. A broad number of metaheuristics are inspired from nature, specially from biology. These bio-inspired metaheuristics are easy to

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Cirello, Antonino, Tommaso Ingrassia, Antonio Mancuso, Giuseppe Marannano, Agostino Igor Mirulla, and Vito Ricotta. "Evaluating the Efficiency of Nature-Inspired Algorithms for Finite Element Optimization in the ANSYS Environment." Applied Sciences 15, no. 12 (2025): 6750. https://doi.org/10.3390/app15126750.

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Nature-inspired metaheuristics have proven effective for addressing complex structural optimization challenges where traditional deterministic or gradient-based methods often fall short. This study investigates the feasibility and benefits of embedding three prominent metaheuristic algorithms, the Genetic Algorithm (GA), the Firefly Algorithm (FA), and the Group Search Optimizer (GSO) embedded into the ANSYS Parametric Design Language (APDL). The performance of each optimizer was assessed in three case studies. The first two are spatial truss structures, one comprising 22 bars and the other 25

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Roeva, Olympia, Dafina Zoteva, and Velislava Lyubenova. "Escherichia coli Cultivation Process Modelling Using ABC-GA Hybrid Algorithm." Processes 9, no. 8 (2021): 1418. http://dx.doi.org/10.3390/pr9081418.

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In this paper, the artificial bee colony (ABC) algorithm is hybridized with the genetic algorithm (GA) for a model parameter identification problem. When dealing with real-world and large-scale problems, it becomes evident that concentrating on a sole metaheuristic algorithm is somewhat restrictive. A skilled combination between metaheuristics or other optimization techniques, a so-called hybrid metaheuristic, can provide more efficient behavior and greater flexibility. Hybrid metaheuristics combine the advantages of one algorithm with the strengths of another. ABC, based on the foraging behav

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Gulić, Marko, Livia Maglić, and Sanjin Valčić. "Nature Inspired Metaheuristics for Optimizing Problems at a Container Terminal." Pomorstvo 32, no. 1 (2018): 10–20. http://dx.doi.org/10.31217/p.32.1.16.

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Nowadays, maritime transport is the backbone of the international trade of goods. Therefore, seaports play a very important role in global transport. The use of containers is significantly represented in the maritime transport. Considering the increased number of container shipments in the global transport, seaport container terminals have to be adapted to a new situation and provide the best possible service of container transfer by reducing the transfer cost and the container transit time. Therefore, there is a need for optimization of the whole container transport process within the termina

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Wang, Chia-Hung, Kun Hu, Xiaojing Wu, and Yufeng Ou. "Rethinking Metaheuristics: Unveiling the Myth of “Novelty” in Metaheuristic Algorithms." Mathematics 13, no. 13 (2025): 2158. https://doi.org/10.3390/math13132158.

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In recent decades, the rapid development of metaheuristic algorithms has outpaced theoretical understanding, with experimental evaluations often overshadowing rigorous analysis. While nature-inspired optimization methods show promise for various applications, their effectiveness is often limited by metaphor-driven design, structural biases, and a lack of sufficient theoretical foundation. This paper systematically examines the challenges in developing robust, generalizable optimization techniques, advocating for a paradigm shift toward modular, transparent frameworks. A comprehensive review of

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Santamaría, J., M. L. Rivero-Cejudo, M. A. Martos-Fernández, and F. Roca. "An Overview on the Latest Nature-Inspired and Metaheuristics-Based Image Registration Algorithms." Applied Sciences 10, no. 6 (2020): 1928. http://dx.doi.org/10.3390/app10061928.

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The development of automated image registration (IR) methods is a well-known issue within the computer vision (CV) field and it has been largely addressed from multiple viewpoints. IR has been applied to a high number of real-world scenarios ranging from remote sensing to medical imaging, artificial vision, and computer-aided design. In the last two decades, there has been an outstanding interest in the application of new optimization approaches for dealing with the main drawbacks present in the early IR methods, e.g., the Iterative Closest Point (ICP) algorithm. In particular, nature-inspired

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Yang, Xin-She, Suash Deb, Simon Fong, Xingshi He, and Yu-Xin Zhao. "From Swarm Intelligence to Metaheuristics: Nature-Inspired Optimization Algorithms." Computer 49, no. 9 (2016): 52–59. http://dx.doi.org/10.1109/mc.2016.292.

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Gautam, Raj Kumar. "Nature Inspired Metaheuristic based Optimization." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem32390.

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This paper is a comprehensive study on nature inspired Hyperparameter optimization, with a distinct focus on Honey Badger Algorithm, along with Aquila Optimizer algorithm. The study involves in-depth analysis of the above algorithms, their weaknesses and strengths and comparing them with the theoretical advantages. The implementation of these algorithms, This paper demonstrate the promise of these algorithms on optimization of Hyperparameters like learning rate, number of hidden layers for our various datasets. The findings of this paper show that HBA and Aquila Optimization algorithms offer p

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Mehmood, Khizer, Naveed Ishtiaq Chaudhary, Zeshan Aslam Khan, et al. "Dwarf Mongoose Optimization Metaheuristics for Autoregressive Exogenous Model Identification." Mathematics 10, no. 20 (2022): 3821. http://dx.doi.org/10.3390/math10203821.

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Nature-inspired metaheuristic algorithms have gained great attention over the last decade due to their potential for finding optimal solutions to different optimization problems. In this study, a metaheuristic based on the dwarf mongoose optimization algorithm (DMOA) is presented for the parameter estimation of an autoregressive exogenous (ARX) model. In the DMOA, the set of candidate solutions were stochastically created and improved using only one tuning parameter. The performance of the DMOA for ARX identification was deeply investigated in terms of its convergence speed, estimation accurac

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Oliveira, Paulo Moura, EJ Solteiro Pires, José Boaventura-Cunha, and Tatiana Martins Pinho. "Review of nature and biologically inspired metaheuristics for greenhouse environment control." Transactions of the Institute of Measurement and Control 42, no. 12 (2020): 2338–58. http://dx.doi.org/10.1177/0142331220909010.

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A significant number of search and optimisation techniques whose principles seek inspiration from nature and biology phenomena have been proposed in the last decades. These methods have been successfully applied to solve a wide range of engineering problems. This is also the case of greenhouse environment control, which has been incorporating this type of techniques into its design. This paper addresses evolutionary and bio-inspired methods in the context of greenhouse environment control. Algorithm principles for reference techniques are reviewed, namely: simulated annealing, genetic algorith

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Bejinariu, Silviu, and Hariton Costin. "A Comparison of Some Nature-Inspired Optimization Metaheuristics Applied in Biomedical Image Registration." Methods of Information in Medicine 57, no. 05/06 (2018): 280–86. http://dx.doi.org/10.1055/s-0038-1673693.

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Computational Intelligence Re-meets Medical Image Processing Analysis of Machine Learning Algorithms for Diagnosis of Diffuse Lung Diseases Background In the last decades, new optimization methods based on the nature's intelligence were developed. These metaheuristics can find a nearly optimal solution faster than other traditional algorithms even for high-dimensional optimization problems. All these algorithms have a similar structure, the difference being made by the strategies used during the evolutionary process. Objectives A set of three nature-inspired algorithms, including Cuckoo Search

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Pravesh Patel, Et al. "Performance Evaluation of Nature-Inspired Metaheuristic Approaches for Single Document Text Summarization." International Journal on Recent and Innovation Trends in Computing and Communication 12, no. 1 (2024): 137–44. http://dx.doi.org/10.17762/ijritcc.v12i1.9776.

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In today era, day by day huge amount of data is collected on internet. The reading of text document or retrieving important information are time consuming process, so there is need for introducing effective text summarization technique. Text summarization, is the process of retrieving key information from lengthy document, its plays an essential role in information retrieval and content extraction. The paper we presented a comprehensive examination of nature-inspired metaheuristic algorithms, such as firefly, Cuckoo Search(CS) and Particle Swarm Optimization (PSO) to improve text summarization

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Meilinda, Fitriani Nur Maghfiroh, Agung Ngurah Perwira Redi Anak, Ong Janice, and Rausyan Fikri Muhamad. "Cuckoo search algorithm for construction site layout planning." International Journal of Artificial Intelligence (IJ-AI) 12, no. 2 (2023): 851–60. https://doi.org/10.11591/ijai.v12.i2.pp851-860.

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A novel metaheuristic optimization algorithm based on cuckoo search algorithm (CSA) is presented to solve the construction site layout planning problem (CSLP). CSLP is a complex optimization problem with various applications, such as plant layout, construction site layout, and computer chip layout. Many researchers have investigated the CSLP by applying many algorithms in an exact or heuristic approach. Although both methods yield a promising result, technically, nature-inspired algorithms demonstrate high achievement in successful percentage. In the last two decades, researchers have been dev

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Singh, Dharmpal. "A New Bio-Inspired Social Spider Algorithm." International Journal of Applied Metaheuristic Computing 12, no. 1 (2021): 79–93. http://dx.doi.org/10.4018/ijamc.2021010105.

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The concept of bio-inspired algorithms is used in real-world problems to search the efficient problem-solving methods. Evolutionary computation and swarm intelligence are outstanding examples of nature-inspired solution techniques of metahuristics. In this paper, an effort has been made to propose a modified social spider algorithm to solve global optimization problems in the real world. Social spiders used the foraging strategy, vibrations on the spider web to determine the positions of prey. The selection of vibration, estimated new position and calculation of the fitness function, has been

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Li, Juan, Qing An, Hong Lei, Qian Deng, and Gai-Ge Wang. "Survey of Lévy Flight-Based Metaheuristics for Optimization." Mathematics 10, no. 15 (2022): 2785. http://dx.doi.org/10.3390/math10152785.

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Lévy flight is a random walk mechanism which can make large jumps at local locations with a high probability. The probability density distribution of Lévy flight was characterized by sharp peaks, asymmetry, and trailing. Its movement pattern alternated between frequent short-distance jumps and occasional long-distance jumps, which can jump out of local optimal and expand the population search area. The metaheuristic algorithms are inspired by nature and applied to solve NP-hard problems. Lévy flight is used as an operator in the cuckoo algorithm, monarch butterfly optimization, and moth search

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Q. Salih, Sinan, and Abdul Rahman A. Alsewari. "Solving large-scale problems using multi-swarm particle swarm approach." International Journal of Engineering & Technology 7, no. 3 (2018): 1725. http://dx.doi.org/10.14419/ijet.v7i3.14742.

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Several metaheuristics have been previously proposed and several improvements have been implemented as well. Most of these methods were either inspired by nature or by the behavior of certain swarms such as birds, ants, bees, or even bats. In the metaheuristics, two key components (exploration and exploitation) are significant and their interaction can significantly affect the efficiency of a metaheuristic. How-ever, there is no rule on how to balance these important components. In this paper, a new balancing mechanism based on multi-swarm approach is proposed for balancing exploration and exp

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Chandra, Agung, and Aulia Naro. "NATURE INSPIRED METAHEURISTICS COMPARATIVE STUDY TO SOLVE TRAVELING SALESMAN PROBLEM." Journal of Engineering and Management in Industrial System 9, no. 2 (2021): 1–10. http://dx.doi.org/10.21776/ub.jemis.2021.009.02.1.

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There are numerous optimization method to solve the traveling salesman problem, TSP. One of methods is metaheuristics which is the state of the art algorithm that can solve the large and complex problem. In this research, three of well-known nature inspired population based metaheuristics algorithm: Ant Colony Optimization – ACO, Artificial Bee Colony – ABC and Particle Swarm Optimization – PSO are compared to solve the 29 destinations by using Matlab program. The ACO produces the shortest distance, 94 kilometers and is more efficient than ABC and PSO methods.

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Yazdani, Maziar, and Fariborz Jolai. "Lion Optimization Algorithm (LOA): A nature-inspired metaheuristic algorithm." Journal of Computational Design and Engineering 3, no. 1 (2015): 24–36. http://dx.doi.org/10.1016/j.jcde.2015.06.003.

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Abstract During the past decade, solving complex optimization problems with metaheuristic algorithms has received considerable attention among practitioners and researchers. Hence, many metaheuristic algorithms have been developed over the last years. Many of these algorithms are inspired by various phenomena of nature. In this paper, a new population based algorithm, the Lion Optimization Algorithm (LOA), is introduced. Special lifestyle of lions and their cooperation characteristics has been the basic motivation for development of this optimization algorithm. Some benchmark problems are sele

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Yildiz, Betül Sultan, Pranav Mehta, Sadiq M. Sait, Natee Panagant, Sumit Kumar, and Ali Riza Yildiz. "A new hybrid artificial hummingbird-simulated annealing algorithm to solve constrained mechanical engineering problems." Materials Testing 64, no. 7 (2022): 1043–50. http://dx.doi.org/10.1515/mt-2022-0123.

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Abstract Nature-inspired algorithms known as metaheuristics have been significantly adopted by large-scale organizations and the engineering research domain due their several advantages over the classical optimization techniques. In the present article, a novel hybrid metaheuristic algorithm (HAHA-SA) based on the artificial hummingbird algorithm (AHA) and simulated annealing problem is proposed to improve the performance of the AHA. To check the performance of the HAHA-SA, it was applied to solve three constrained engineering design problems. For comparative analysis, the results of all consi

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Sanchez-Cartas, J. Manuel, and Ines P. Sancristobal. "Limitations of Nature-Inspired Algorithms for Pricing on Digital Platforms." Electronics 11, no. 23 (2022): 3927. http://dx.doi.org/10.3390/electronics11233927.

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Digital platforms have begun to rely more on algorithms to perform basic tasks such as pricing. These platforms must set prices that coordinate two or more sides that need each other in some way (e.g., developers and users or buyers and sellers). Therefore, it is essential to form correct expectations about how both sides behave. The purpose of this paper was to study the effect of different levels of information on two biology-inspired metaheuristics (differential evolution and particle swarm optimization algorithms) that were programmed to set prices on multisided platforms. We assumed that

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Ahmed, Rasel, Amril Nazir, Shuhaimi Mahadzir, Mohammad Shorfuzzaman, and Jahedul Islam. "Niching Grey Wolf Optimizer for Multimodal Optimization Problems." Applied Sciences 11, no. 11 (2021): 4795. http://dx.doi.org/10.3390/app11114795.

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Metaheuristic algorithms are widely used for optimization in both research and the industrial community for simplicity, flexibility, and robustness. However, multi-modal optimization is a difficult task, even for metaheuristic algorithms. Two important issues that need to be handled for solving multi-modal problems are (a) to categorize multiple local/global optima and (b) to uphold these optima till the ending. Besides, a robust local search ability is also a prerequisite to reach the exact global optima. Grey Wolf Optimizer (GWO) is a recently developed nature-inspired metaheuristic algorith

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Caselli, Nicolás, Ricardo Soto, Broderick Crawford, Sergio Valdivia, Elizabeth Chicata, and Rodrigo Olivares. "Dynamic Population on Bio-Inspired Algorithms Using Machine Learning for Global Optimization." Biomimetics 9, no. 1 (2023): 7. http://dx.doi.org/10.3390/biomimetics9010007.

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In the optimization field, the ability to efficiently tackle complex and high-dimensional problems remains a persistent challenge. Metaheuristic algorithms, with a particular emphasis on their autonomous variants, are emerging as promising tools to overcome this challenge. The term “autonomous” refers to these variants’ ability to dynamically adjust certain parameters based on their own outcomes, without external intervention. The objective is to leverage the advantages and characteristics of an unsupervised machine learning clustering technique to configure the population parameter with auton

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Ilyass, Mzili, Essaid Riffi Mohammed, and Benzakri Fatiha. "Discrete penguins search optimization algorithm to solve flow shop scheduling problem." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 4 (2020): 4426–35. https://doi.org/10.11591/ijece.v10i4.pp4426-4435.

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Flow shop scheduling problem is one of the most classical NP-hard optimization problem. Which aims to find the best planning that minimizes the makespan (total completion time) of a set of tasks in a set of machines with certain constraints. In this paper, we propose a new nature inspired metaheuristic to solve the flow shop scheduling problem (FSSP), called penguins search optimization algorithm (PeSOA) based on collaborative hunting strategy of penguins.The operators and parameter values of PeSOA redefined to solve this problem. The performance of the penguins search optimization algorithm i

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Munien, Chanaleä, and Absalom E. Ezugwu. "Metaheuristic algorithms for one-dimensional bin-packing problems: A survey of recent advances and applications." Journal of Intelligent Systems 30, no. 1 (2021): 636–63. http://dx.doi.org/10.1515/jisys-2020-0117.

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Abstract The bin-packing problem (BPP) is an age-old NP-hard combinatorial optimization problem, which is defined as the placement of a set of different-sized items into identical bins such that the number of containers used is optimally minimized. Besides, different variations of the problem do exist in practice depending on the bins dimension, placement constraints, and priority. More so, there are several important real-world applications of the BPP, especially in cutting industries, transportation, warehousing, and supply chain management. Due to the practical relevance of this problem, re

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Bhasin, Namisha, Sanjay Kumar Sharma, and Rajesh Mishra. "Deep Learning-Based Authentication Using Keystroke-Dynamics." Journal of Neonatal Surgery 14, no. 4 (2025): 524–35. https://doi.org/10.63682/jns.v14i4.7547.

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Keystroke dynamics where a user is authenticated based on his/her typing patterns. It is considered as best solution to authenticate a user as this problem is solved by considering behavioural characteristics which is very difficult to copy. In this research paper we solved the problem of static keystroke dynamics by deep learning approach.in this paper we use the concept of quantile transformation which reduces the impact of outliers. For pattern reorganization, various optimization algorithms are used. For global pattern recognition various Metaheuristics algorithms and for local pattern ADA

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Sa'ad, Suleiman, Muhammed Abdullah, Azizol Abdullah, and Fahrul Hakim Ayob. "Symbiotic Organisms Search Optimization Algorithm in Cloud Computing: A Nature-inspired Meta-heuristic." Systematic Literature Review and Meta-Analysis Journal 3, no. 1 (2022): 1–8. http://dx.doi.org/10.54480/slrm.v3i1.29.

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In the past few years nature-inspired algorithms are experiencing rapid growth where most optimisation problems in different domains are addressed using it. As a result of this development come the issue of handling a complex optimisation problem within a short period remains very difficult. Symbiotic organisms search (SOS) algorithm is one of the nature-inspired metaheuristics that mimics the symbiotic association of organisms in an ecosystem. This paper proposes to investigate symbiotic organisms search algorithms used in handling various optimisation problems in different fields to bring ou

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Bansal, Shonak, Neena Gupta, and Arun Kumar Singh. "Nature–inspired metaheuristic algorithms to find near–OGR sequences for WDM channel allocation and their performance comparison." Open Mathematics 15, no. 1 (2017): 520–47. http://dx.doi.org/10.1515/math-2017-0045.

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Abstract Nowadays, nature–inspired metaheuristic algorithms are most powerful optimizing algorithms for solving the NP–complete problems. This paper proposes three approaches to find near–optimal Golomb ruler sequences based on nature–inspired algorithms in a reasonable time. The optimal Golomb ruler (OGR) sequences found their application in channel–allocation method that allows suppression of the crosstalk due to four–wave mixing in optical wavelength division multiplexing systems. The simulation results conclude that the proposed nature–inspired metaheuristic optimization algorithms are sup

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Ikotun, Abiodun M., Mubarak S. Almutari, and Absalom E. Ezugwu. "K-Means-Based Nature-Inspired Metaheuristic Algorithms for Automatic Data Clustering Problems: Recent Advances and Future Directions." Applied Sciences 11, no. 23 (2021): 11246. http://dx.doi.org/10.3390/app112311246.

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K-means clustering algorithm is a partitional clustering algorithm that has been used widely in many applications for traditional clustering due to its simplicity and low computational complexity. This clustering technique depends on the user specification of the number of clusters generated from the dataset, which affects the clustering results. Moreover, random initialization of cluster centers results in its local minimal convergence. Automatic clustering is a recent approach to clustering where the specification of cluster number is not required. In automatic clustering, natural clusters e

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Abderrahim, Imène Ait, and Lakhdar Loukil. "Hybrid Approach for Solving the Q3AP." International Journal of Swarm Intelligence Research 12, no. 1 (2021): 98–114. http://dx.doi.org/10.4018/ijsir.2021010106.

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Metaheuristics algorithms are competitive methods for solving assignment problems. This paper reports on nature inspired algorithms approach which is the particle swarm optimization (PSO) method hybrid with a local search (LS) algorithm for solving the quadratic three-dimensional assignment problem (Q3AP) where population-based metaheuristics like PSO or GA failed to solve. Q3AP is one of the combinatorial problems proven to be NP-Hard. It is an extension of the quadratic assignment problem (QAP). Solving the Q3AP consists of finding an optimal symbol mapping over two vectors, whereas solving

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Caraffini, Fabio, and Giovanni Iacca. "The SOS Platform: Designing, Tuning and Statistically Benchmarking Optimisation Algorithms." Mathematics 8, no. 5 (2020): 785. http://dx.doi.org/10.3390/math8050785.

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We present Stochastic Optimisation Software (SOS), a Java platform facilitating the algorithmic design process and the evaluation of metaheuristic optimisation algorithms. SOS reduces the burden of coding miscellaneous methods for dealing with several bothersome and time-demanding tasks such as parameter tuning, implementation of comparison algorithms and testbed problems, collecting and processing data to display results, measuring algorithmic overhead, etc. SOS provides numerous off-the-shelf methods including: (1) customised implementations of statistical tests, such as the Wilcoxon rank-su

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Virk, Amandeep Kaur, and Kawaljeet Singh. "Solving Two-Dimensional Rectangle Packing Problem Using Nature-Inspired Metaheuristic Algorithms." Journal of Industrial Integration and Management 03, no. 02 (2018): 1850009. http://dx.doi.org/10.1142/s2424862218500094.

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This paper applies cuckoo search and bat metaheuristic algorithms to solve two-dimensional non-guillotine rectangle packing problem. These algorithms have not been found to be used before in the literature to solve this important industrial problem. The purpose of this work is to explore the potential of these new metaheuristic methods and to check whether they can contribute in enhancing the performance of this problem. Standard benchmark test data has been used to solve the problem. The performance of these algorithms was measured and compared with genetic algorithm and tabu search technique

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Mello-Roman, Jorge Daniel, and Adolfo Hernandez. "KPLS Optimization With Nature-Inspired Metaheuristic Algorithms." IEEE Access 8 (2020): 157482–92. http://dx.doi.org/10.1109/access.2020.3019771.

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Shahab, Muhammad Luthfi, and Mohammad Isa Irawan. "Sequence Alignment Using Nature-Inspired Metaheuristic Algorithms." International Journal of Computing Science and Applied Mathematics 3, no. 1 (2017): 27. http://dx.doi.org/10.12962/j24775401.v3i1.2118.

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Bouteldja, Mohamed Abdou, Mohamed Baadeche, and Mohamed Batouche. "Multilevel Thresholding for Image Segmentation Based on Cellular Metaheuristics." International Journal of Applied Metaheuristic Computing 9, no. 4 (2018): 1–32. http://dx.doi.org/10.4018/ijamc.2018100101.

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This article describes how multilevel thresholding image segmentation is a process used to partition an image into well separated regions. It has various applications such as object recognition, edge detection, and particle counting, etc. However, it is computationally expensive and time consuming. To alleviate these limitations, nature inspired metaheuristics are widely used to reduce the computational complexity of such problem. In this article, three cellular metaheuristics namely cellular genetic algorithm (CGA), cellular particle swarm optimization (CPSO) and cellular differential evoluti

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Goudhaman, M. "Cheetah chase algorithm (CCA): a nature-inspired metaheuristic algorithm." International Journal of Engineering & Technology 7, no. 3 (2018): 1804. http://dx.doi.org/10.14419/ijet.v7i3.18.14616.

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In recent years, appreciable attention among analysts to take care of the extraordinary enhancement issues utilizing metaheuristic algorithms in the domain area of Swarm Intelligence. Many metaheuristic algorithms have been developed by inspiring various nature phenomena’s. Exploration and exploitation are distinctive capacities and confine each other, along these lines, customary calculations require numerous parameters and bunches of expenses to accomplish the adjust, and furthermore need to modify parameters for various enhancement issues. In this paper, another populace based algorithm, th

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Kozdrowski, Stanisław, Mateusz Żotkiewicz, Kacper Wnuk, Arkadiusz Sikorski, and Sławomir Sujecki. "A Comparative Evaluation of Nature Inspired Algorithms for Telecommunication Network Design." Applied Sciences 10, no. 19 (2020): 6840. http://dx.doi.org/10.3390/app10196840.

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The subject of the study was an application of nature-inspired metaheuristic algorithms to node configuration optimization in optical networks. The main objective of the optimization was to minimize capital expenditure, which includes the costs of optical node resources, such as transponders and amplifiers used in a new generation of optical networks. For this purpose a model that takes into account the physical phenomena in the optical network is proposed. Selected nature-inspired metaheuristic algorithms were implemented and compared with a reference, deterministic algorithm, based on linear

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Yang, Xin-She. "Chaos-Enhanced Firefly Algorithm with Automatic Parameter Tuning." International Journal of Swarm Intelligence Research 2, no. 4 (2011): 1–11. http://dx.doi.org/10.4018/jsir.2011100101.

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Many metaheuristic algorithms are nature-inspired, and most are population-based. Particle swarm optimization is a good example as an efficient metaheuristic algorithm. Inspired by PSO, many new algorithms have been developed in recent years. For example, firefly algorithm was inspired by the flashing behaviour of fireflies. In this paper, the author extends the standard firefly algorithm further to introduce chaos-enhanced firefly algorithm with automatic parameter tuning, which results in two more variants of FA. The author first compares the performance of these algorithms, and then uses th

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Malik, Shairyar, Tallha Akram, Muhammad Awais, et al. "An Improved Skin Lesion Boundary Estimation for Enhanced-Intensity Images Using Hybrid Metaheuristics." Diagnostics 13, no. 7 (2023): 1285. http://dx.doi.org/10.3390/diagnostics13071285.

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The demand for the accurate and timely identification of melanoma as a major skin cancer type is increasing daily. Due to the advent of modern tools and computer vision techniques, it has become easier to perform analysis. Skin cancer classification and segmentation techniques require clear lesions segregated from the background for efficient results. Many studies resolve the matter partly. However, there exists plenty of room for new research in this field. Recently, many algorithms have been presented to preprocess skin lesions, aiding the segmentation algorithms to generate efficient outcom

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Syafruddin, Willa Ariela, Rio Mukhtarom Paweroi, and Mario Köppen. "Behavior Selection Metaheuristic Search Algorithm for the Pollination Optimization: A Simulation Case of Cocoa Flowers." Algorithms 14, no. 8 (2021): 230. http://dx.doi.org/10.3390/a14080230.

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Since nature is an excellent source of inspiration for optimization methods, many optimization algorithms have been proposed, are inspired by nature, and are modified to solve various optimization problems. This paper uses metaheuristics in a new field inspired by nature; more precisely, we use pollination optimization in cocoa plants. The cocoa plant was chosen as the object since its flower type differs from other kinds of flowers, for example, by using cross-pollination. This complex relationship between plants and pollinators also renders pollination a real-world problem for chocolate prod

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Priyadarshini, Ishaani. "Dendritic Growth Optimization: A Novel Nature-Inspired Algorithm for Real-World Optimization Problems." Biomimetics 9, no. 3 (2024): 130. http://dx.doi.org/10.3390/biomimetics9030130.

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In numerous scientific disciplines and practical applications, addressing optimization challenges is a common imperative. Nature-inspired optimization algorithms represent a highly valuable and pragmatic approach to tackling these complexities. This paper introduces Dendritic Growth Optimization (DGO), a novel algorithm inspired by natural branching patterns. DGO offers a novel solution for intricate optimization problems and demonstrates its efficiency in exploring diverse solution spaces. The algorithm has been extensively tested with a suite of machine learning algorithms, deep learning alg

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Xu, Yiqi, Qiongqiong Li, Xuan Xu, Jiafu Yang, and Yong Chen. "Research Progress of Nature-Inspired Metaheuristic Algorithms in Mobile Robot Path Planning." Electronics 12, no. 15 (2023): 3263. http://dx.doi.org/10.3390/electronics12153263.

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The research of mobile robot path planning has shifted from the static environment to the dynamic environment, from the two-dimensional environment to the high-dimensional environment, and from the single-robot system to the multi-robot system. As the core technology for mobile robots to realize autonomous positioning and navigation, path-planning technology should plan collision-free and smooth paths for mobile robots in obstructed environments, which requires path-planning algorithms with a certain degree of intelligence. Metaheuristic algorithms are widely used in various optimization probl

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Carreon-Ortiz, Hector, Fevrier Valdez, and Oscar Castillo. "A New Discrete Mycorrhiza Optimization Nature-Inspired Algorithm." Axioms 11, no. 8 (2022): 391. http://dx.doi.org/10.3390/axioms11080391.

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This paper presents the discrete version of the Mycorrhiza Tree Optimization Algorithm (MTOA), using the Lotka–Volterra Discrete Equation System (LVDES) formed by the Predator–Prey, Cooperative and Competitive Models. The Discrete Mycorrhizal Optimization Algorithm (DMOA) is a stochastic metaheuristic that integrates randomness in its search processes. These algorithms are inspired by nature, specifically by the symbiosis between plant roots and a fungal network called the Mycorrhizal Network (MN). The communication in the network is performed using chemical signals of environmental conditions

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Valdez, Fevrier, Oscar Castillo, and Patricia Melin. "Bio-Inspired Algorithms and Its Applications for Optimization in Fuzzy Clustering." Algorithms 14, no. 4 (2021): 122. http://dx.doi.org/10.3390/a14040122.

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In recent years, new metaheuristic algorithms have been developed taking as reference the inspiration on biological and natural phenomena. This nature-inspired approach for algorithm development has been widely used by many researchers in solving optimization problems. These algorithms have been compared with the traditional ones and have demonstrated to be superior in many complex problems. This paper attempts to describe the algorithms based on nature, which are used in optimizing fuzzy clustering in real-world applications. We briefly describe the optimization methods, the most cited ones,

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Singh, Gagan Deep, Sunil Kumar, Hammam Alshazly, Sahar Ahmed Idris, Madhushi Verma, and Samih M. Mostafa. "A Novel Routing Protocol for Realistic Traffic Network Scenarios in VANET." Wireless Communications and Mobile Computing 2021 (December 9, 2021): 1–12. http://dx.doi.org/10.1155/2021/7817249.

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The vehicular ad hoc network (VANET) has traditional routing protocols that evolved from mobile ad hoc networks (MANET). The standard routing protocols of VANET are geocast, topology, broadcast, geographic, and cluster-based routing protocols. They have their limitations and are not suitable for all types of VANET traffic scenarios. Hence, metaheuristics algorithms like evolutionary, trajectory, nature-inspired, and ancient-inspired algorithms can be integrated with standard routing algorithms of VANET to achieve optimized routing performance results in desired VANET traffic scenarios. This pa

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