Статті в журналах: "NATURE INSPIRED ALGORITHM"

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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 (June 16, 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 selected from the literature, and the solution of the proposed algorithm has been compared with those of some well-known and newest meta-heuristics for these problems. The obtained results confirm the high performance of the proposed algorithm in comparison to the other algorithms used in this paper.

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Goudhaman, M. "Cheetah chase algorithm (CCA): a nature-inspired metaheuristic algorithm." International Journal of Engineering & Technology 7, no. 3 (August 22, 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, the Cheetah Chase Algorithm (CCA), is presented. Distinctive features of Cheetah and their characteristics has been the essential motivation for advancement of this optimization algorithm. Cheetah Chase Algorithm (CCA) has awesome capacities both in exploitation and exploration, is proposed to address these issues. To start with, CCA endeavours to locate the optimal solution in the assigned hunt territory. It at that point utilizes history data to pursue its prey. CCA can, hence, decide the situation of the worldwide ideal. CCA accomplishes solid exploitation and exploration with these highlights. Additionally, as indicated by various issues, CCA executes versatile parameter change. The self-examination and analysis of this exploration show that each CCA capacity can have different beneficial outcomes, while the execution correlation exhibits CCAs predominance over conventional metaheuristic algorithms. The proposed Cheetah Chase Algorithm is developed by the process of hunting and chasing of Cheetah to capture its prey with the parameters of high speed, velocity and greater accelerations.

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Abualigah, Laith, Amir H. Gandomi, Mohamed Abd Elaziz, Abdelazim G. Hussien, Ahmad M. Khasawneh, Mohammad Alshinwan, and Essam H. Houssein. "Nature-Inspired Optimization Algorithms for Text Document Clustering—A Comprehensive Analysis." Algorithms 13, no. 12 (December 18, 2020): 345. http://dx.doi.org/10.3390/a13120345.

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Text clustering is one of the efficient unsupervised learning techniques used to partition a huge number of text documents into a subset of clusters. In which, each cluster contains similar documents and the clusters contain dissimilar text documents. Nature-inspired optimization algorithms have been successfully used to solve various optimization problems, including text document clustering problems. In this paper, a comprehensive review is presented to show the most related nature-inspired algorithms that have been used in solving the text clustering problem. Moreover, comprehensive experiments are conducted and analyzed to show the performance of the common well-know nature-inspired optimization algorithms in solving the text document clustering problems including Harmony Search (HS) Algorithm, Genetic Algorithm (GA), Particle Swarm Optimization (PSO) Algorithm, Ant Colony Optimization (ACO), Krill Herd Algorithm (KHA), Cuckoo Search (CS) Algorithm, Gray Wolf Optimizer (GWO), and Bat-inspired Algorithm (BA). Seven text benchmark datasets are used to validate the performance of the tested algorithms. The results showed that the performance of the well-known nurture-inspired optimization algorithms almost the same with slight differences. For improvement purposes, new modified versions of the tested algorithms can be proposed and tested to tackle the text clustering problems.

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Gencal, Mashar, and Mustafa Oral. "Roosters Algorithm: A Novel Nature-Inspired Optimization Algorithm." Computer Systems Science and Engineering 42, no. 2 (2022): 727–37. http://dx.doi.org/10.32604/csse.2022.023018.

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Kumar, Deepak, Sushil Kumar, Rohit Bansal, and Parveen Singla. "A Survey to Nature Inspired Soft Computing." International Journal of Information System Modeling and Design 8, no. 2 (April 2017): 112–33. http://dx.doi.org/10.4018/ijismd.2017040107.

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This article describes how swarm intelligence (SI) and bio-inspired techniques shape in-vogue topics in the advancements of the latest algorithms. These algorithms can work on the basis of SI, using physical, chemical and biological frameworks. The authors can name these algorithms as SI-based, inspired by biology, physics and chemistry as per the basic concept behind the particular algorithm. A couple of calculations have ended up being exceptionally effective and consequently have turned out to be the mainstream devices for taking care of real-world issues. In this article, the reason for this survey is to show a moderately complete list of the considerable number of algorithms in order to boost research in these algorithms. This article discusses Ant Colony Optimization (ACO), the Cuckoo Search, the Firefly Algorithm, Particle Swarm Optimization and Genetic Algorithms in detail. For ACO a real-time problem, known as Travelling Salesman Problem, is considered while for other algorithms a min-sphere problem is considered, which is well known for comparison of swarm techniques.

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Hussein, Eslam, Ahmed Ibrahem Hafez, Aboul Ella Hassanien, and Aly A. Fahmy. "Nature inspired algorithms for solving the community detection problem." Logic Journal of the IGPL 25, no. 6 (October 26, 2017): 902–14. http://dx.doi.org/10.1093/jigpal/jzx043.

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Abstract Nature inspired Swarm algorithms have proven to be effective in solving recent complex optimization problems. Comparing such algorithm is a difficult task due to many facts, the nature of the swarm, the nature of the optimization problem itself and number of controlling parameters of the swarm algorithm. In this work we compared two recent swarm algorithms applied to the community detection problem which are the Bat Algorithm (BA) and Artificial Fish Swarm Algorithm (AFSA). Community detection is an active problem in social network analysis. The problem of detecting communities can be represented as an optimization problem where a quality fitness function that captures the intuition of a community as a group of nodes with better internal connectivity than external connectivity is chosen to be optimized. We also investigated the application of the BA and AFSA in solving the community section problem. And introduced a comparative analysis between the two algorithms and other well-known methods. The study show the effectiveness and the limitations of both algorithms.

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Carreon-Ortiz, Hector, Fevrier Valdez, and Oscar Castillo. "A New Discrete Mycorrhiza Optimization Nature-Inspired Algorithm." Axioms 11, no. 8 (August 9, 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 and hazards, the exchange of resources, such as Carbon Dioxide (CO2) that plants perform through photosynthesis to the MN and to other seedlings or growing plants. The MN provides water (H2O) and nutrients to plants that may or may not be of the same species; therefore, the colonization of plants in arid lands would not have been possible without the MN. In this work, we performed a comparison with the CEC-2013 mathematical functions between MTOA and DMOA by conducting Hypothesis Tests to obtain the efficiency and performance of the algorithms, but in future research we will also propose optimization experiments in Neural Networks and Fuzzy Systems to verify with which methods these algorithms perform better.

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Karunanidy, Dinesh, Subramanian Ramalingam, Ankur Dumka, Rajesh Singh, Mamoon Rashid, Anita Gehlot, Sultan S. Alshamrani, and Ahmed Saeed AlGhamdi. "JMA: Nature-Inspired Java Macaque Algorithm for Optimization Problem." Mathematics 10, no. 5 (February 23, 2022): 688. http://dx.doi.org/10.3390/math10050688.

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In recent years, optimization problems have been intriguing in the field of computation and engineering due to various conflicting objectives. The complexity of the optimization problem also dramatically increases with respect to a complex search space. Nature-Inspired Optimization Algorithms (NIOAs) are becoming dominant algorithms because of their flexibility and simplicity in solving the different kinds of optimization problems. Hence, the NIOAs may be struck with local optima due to an imbalance in selection strategy, and which is difficult when stabilizing exploration and exploitation in the search space. To tackle this problem, we propose a novel Java macaque algorithm that mimics the natural behavior of the Java macaque monkeys. The Java macaque algorithm uses a promising social hierarchy-based selection process and also achieves well-balanced exploration and exploitation by using multiple search agents with a multi-group population, male replacement, and learning processes. Then, the proposed algorithm extensively experimented with the benchmark function, including unimodal, multimodal, and fixed-dimension multimodal functions for the continuous optimization problem, and the Travelling Salesman Problem (TSP) was utilized for the discrete optimization problem. The experimental outcome depicts the efficiency of the proposed Java macaque algorithm over the existing dominant optimization algorithms.

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Rajakumar, B. R. "The Lion's Algorithm: A New Nature-Inspired Search Algorithm." Procedia Technology 6 (2012): 126–35. http://dx.doi.org/10.1016/j.protcy.2012.10.016.

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Maghayreh, Eslam Al, Sallam Abu Al Haija, Faisal Alkhateeb, Shadi Aljawarneh, and Emad Al Shawakfa. "BeesAnts: a new nature-inspired routing algorithm." International Journal of Communication Networks and Distributed Systems 10, no. 1 (2013): 83. http://dx.doi.org/10.1504/ijcnds.2013.050614.

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Faramarzi, Afshin, Mohammad Heidarinejad, Seyedali Mirjalili, and Amir H. Gandomi. "Marine Predators Algorithm: A nature-inspired metaheuristic." Expert Systems with Applications 152 (August 2020): 113377. http://dx.doi.org/10.1016/j.eswa.2020.113377.

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Chmiel, W., P. Kadłuczka, J. Kwiecień, and B. Filipowicz. "A comparison of nature inspired algorithms for the quadratic assignment problem." Bulletin of the Polish Academy of Sciences Technical Sciences 65, no. 4 (August 1, 2017): 513–22. http://dx.doi.org/10.1515/bpasts-2017-0056.

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AbstractThis paper presents an application of the ant algorithm and bees algorithm in optimization of QAP problem as an example of NP-hard optimization problem. The experiments with two types of algorithms: the bees algorithm and the ant algorithm were performed for the test instances of the quadratic assignment problem from QAPLIB, designed by Burkard, Karisch and Rendl. On the basis of the experiments results, an influence of particular elements of algorithms, including neighbourhood size and neighbourhood search method, will be determined.

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Bujok, Petr, Josef Tvrdik, and Radka Polakova. "Nature-Inspired Algorithms in Real-World Optimization Problems." MENDEL 23, no. 1 (June 1, 2017): 7–14. http://dx.doi.org/10.13164/mendel.2017.1.007.

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Eight popular nature inspired algorithms are compared with the blind random search and three advanced adaptive variants of differential evolution (DE) on real-world problems benchmark collected for CEC 2011 algorithms competition. The results show the good performance of the adaptive DE variants and their superiority over the other algorithms in the test problems. Some of the nature-inspired algorithms perform even worse that the blind random search in some problems. This is a strong argument for recommendation for application, where well-verified algorithm successful in competitions should be preferred instead of developing some new algorithms.

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Naik, Bighnaraj, Janmenjoy Nayak, and H. S. Behera. "FLANN + BHO." International Journal of Rough Sets and Data Analysis 5, no. 1 (January 2018): 13–33. http://dx.doi.org/10.4018/ijrsda.2018010102.

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Among some of the competent optimization algorithms, nature inspired algorithms are quite popular due to their flexibility and ease of use in diversified domains. Moreover, balancing between exploration and exploitation is one of the important aspects of nature inspired optimizations. In this paper, a recently developed nature inspired algorithm such as black hole algorithm has been used with the functional link neural network for handling the nonlinearity nature of system identification. Specifically, the proposed hybrid approach is used to solve classification problem. The results of the hybrid approach are compared with some of the other popular competent nature based approaches and found the superiority of the proposed method over others. Also, a brief discussion on the working principles of the black hole algorithm and its available literatures are discussed.

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Dehghani, Mohammad, Štěpán Hubálovský, and Pavel Trojovský. "Cat and Mouse Based Optimizer: A New Nature-Inspired Optimization Algorithm." Sensors 21, no. 15 (July 31, 2021): 5214. http://dx.doi.org/10.3390/s21155214.

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Numerous optimization problems designed in different branches of science and the real world must be solved using appropriate techniques. Population-based optimization algorithms are some of the most important and practical techniques for solving optimization problems. In this paper, a new optimization algorithm called the Cat and Mouse-Based Optimizer (CMBO) is presented that mimics the natural behavior between cats and mice. In the proposed CMBO, the movement of cats towards mice as well as the escape of mice towards havens is simulated. Mathematical modeling and formulation of the proposed CMBO for implementation on optimization problems are presented. The performance of the CMBO is evaluated on a standard set of objective functions of three different types including unimodal, high-dimensional multimodal, and fixed-dimensional multimodal. The results of optimization of objective functions show that the proposed CMBO has a good ability to solve various optimization problems. Moreover, the optimization results obtained from the CMBO are compared with the performance of nine other well-known algorithms including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Teaching-Learning-Based Optimization (TLBO), Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), Marine Predators Algorithm (MPA), Tunicate Swarm Algorithm (TSA), and Teamwork Optimization Algorithm (TOA). The performance analysis of the proposed CMBO against the compared algorithms shows that CMBO is much more competitive than other algorithms by providing more suitable quasi-optimal solutions that are closer to the global optimal.

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Nur Maghfiroh, Meilinda Fitriani, Anak Agung Ngurah Perwira Redi, Janice Ong, and Muhamad Rausyan Fikri. "Cuckoo search algorithm for construction site layout planning." IAES International Journal of Artificial Intelligence (IJ-AI) 12, no. 2 (June 1, 2023): 851. http://dx.doi.org/10.11591/ijai.v12.i2.pp851-860.

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<span lang="EN-US">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 developing a new nature-inspired algorithm for solving different types of optimization problems. The CSA has gained popularity in resolving large and complex issues with promising results compared with other nature-inspired algorithms. However, for solving CSLP, the algorithm based on CSA is still minor. Thus, this study proposed CSA with additional modification in the algorithm mechanism, where the algorithm shows a promising result and can solve CSLP cases.</span>

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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 (September 29, 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 integer programming. For the cases studied the obtained results show that there is a large advantage in using metaheuristic algorithms. In particular, the evolutionary algorithm, the bees algorithm and the harmony search algorithm showed superior performance for the considered data-sets corresponding to large optical networks; the integer programming-based algorithm failed to find an acceptable sub-optimal solution within the assumed maximum computational time. All optimization methods were compared for selected instances of realistic teletransmission networks of different dimensions subject to traffic demand sets extracted from real traffic data.

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Jain, Mohit, Vijander Singh, and Asha Rani. "A novel nature-inspired algorithm for optimization: Squirrel search algorithm." Swarm and Evolutionary Computation 44 (February 2019): 148–75. http://dx.doi.org/10.1016/j.swevo.2018.02.013.

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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 (July 29, 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 problems due to their algorithmic intelligence, and they have become the most effective algorithm to solve complex optimization problems in the field of mobile robot path planning. Based on a comprehensive analysis of existing path-planning algorithms, this paper proposes a new algorithm classification. Based on this classification, we focus on the firefly algorithm (FA) and the cuckoo search algorithm (CS), complemented by the dragonfly algorithm (DA), the whale optimization algorithm (WOA), and the sparrow search algorithm (SSA). During the analysis of the above algorithms, this paper summarizes the current research results of mobile robot path planning and proposes the future development trend of mobile robot path planning.

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Naseri, Narjes Khatoon, Elankovan A. Sundararajan, Masri Ayob, and Amin Jula. "Smart Root Search (SRS): A Novel Nature-Inspired Search Algorithm." Symmetry 12, no. 12 (December 7, 2020): 2025. http://dx.doi.org/10.3390/sym12122025.

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In this paper, a novel heuristic search algorithm called Smart Root Search (SRS) is proposed. SRS employs intelligent foraging behavior of immature, mature and hair roots of plants to explore and exploit the problem search space simultaneously. SRS divides the search space into several subspaces. It thereupon utilizes the branching and drought operations to focus on richer areas of promising subspaces while extraneous ones are not thoroughly ignored. To achieve this, the smart reactions of the SRS model are designed to act based on analyzing the heterogeneous conditions of various sections of different search spaces. In order to evaluate the performance of the SRS, it was tested on a set of known unimodal and multimodal test functions. The results were then compared with those obtained using genetic algorithms, particle swarm optimization, differential evolution and imperialist competitive algorithms and then analyzed statistically. The results demonstrated that the SRS outperformed comparative algorithms for 92% and 82% of the investigated unimodal and multimodal test functions, respectively. Therefore, the SRS is a promising nature-inspired optimization algorithm.

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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 (June 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 techniques which can be found to be used widely in the literature to solve this problem. Good optimal solutions were obtained from all the techniques and the new metaheuristic algorithms performed better than genetic algorithm and tabu search. It was seen that cuckoo search algorithm excels in performance as compared to the other techniques.

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Trojovský, Pavel, and Mohammad Dehghani. "Pelican Optimization Algorithm: A Novel Nature-Inspired Algorithm for Engineering Applications." Sensors 22, no. 3 (January 23, 2022): 855. http://dx.doi.org/10.3390/s22030855.

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Optimization is an important and fundamental challenge to solve optimization problems in different scientific disciplines. In this paper, a new stochastic nature-inspired optimization algorithm called Pelican Optimization Algorithm (POA) is introduced. The main idea in designing the proposed POA is simulation of the natural behavior of pelicans during hunting. In POA, search agents are pelicans that search for food sources. The mathematical model of the POA is presented for use in solving optimization issues. The performance of POA is evaluated on twenty-three objective functions of different unimodal and multimodal types. The optimization results of unimodal functions show the high exploitation ability of POA to approach the optimal solution while the optimization results of multimodal functions indicate the high ability of POA exploration to find the main optimal area of the search space. Moreover, four engineering design issues are employed for estimating the efficacy of the POA in optimizing real-world applications. The findings of POA are compared with eight well-known metaheuristic algorithms to assess its competence in optimization. The simulation results and their analysis show that POA has a better and more competitive performance via striking a proportional balance between exploration and exploitation compared to eight competitor algorithms in providing optimal solutions for optimization problems.

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Sachan, Rohit Kumar, and Dharmender Singh Kushwaha. "A Generalized and Robust Anti-Predatory Nature-Inspired Algorithm for Complex Problems." International Journal of Applied Metaheuristic Computing 10, no. 1 (January 2019): 75–91. http://dx.doi.org/10.4018/ijamc.2019010105.

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This article describes how nature-inspired algorithms (NIAs) have evolved as efficient approaches for addressing the complexities inherent in the optimization of real-world applications. These algorithms are designed to imitate processes in nature that provide some ways of problem solving. Although various nature-inspired algorithms have been proposed by various researchers in the past, a robust and computationally simple NIA is still missing. A novel nature-inspired algorithm that adapts to the anti-predatory behavior of the frog is proposed. The algorithm mimics the self defense mechanism of a frog. Frogs use their reflexes as a means of protecting themselves from the predators. A mathematical formulation of these reflexes forms the core of the proposed approach. The robustness of the proposed algorithm is verified through performance evaluation on sixteen different unconstrained mathematical benchmark functions based on best and worst values as well as mean and standard deviation of the computed results. These functions are representative of different properties and characteristics of the problem domain. The strength and robustness of the proposed algorithm is established through a comparative result analysis with six well-known optimization algorithms, namely: genetic, particle swarm, differential evolution, artificial bee colony, teacher learning and Jaya. The Friedman rank test and the Holm-Sidak test have been used for statistical analysis of obtained results. The proposed algorithm ranks first in the case of mean result and scores second rank in the case of “standard deviation”. This proves the significance of the proposed algorithm.

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Peraza-Vázquez, Hernán, Adrián Peña-Delgado, Prakash Ranjan, Chetan Barde, Arvind Choubey, and Ana Beatriz Morales-Cepeda. "A Bio-Inspired Method for Mathematical Optimization Inspired by Arachnida Salticidade." Mathematics 10, no. 1 (December 29, 2021): 102. http://dx.doi.org/10.3390/math10010102.

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This paper proposes a new meta-heuristic called Jumping Spider Optimization Algorithm (JSOA), inspired by Arachnida Salticidae hunting habits. The proposed algorithm mimics the behavior of spiders in nature and mathematically models its hunting strategies: search, persecution, and jumping skills to get the prey. These strategies provide a fine balance between exploitation and exploration over the solution search space and solve global optimization problems. JSOA is tested with 20 well-known testbench mathematical problems taken from the literature. Further studies include the tuning of a Proportional-Integral-Derivative (PID) controller, the Selective harmonic elimination problem, and a few real-world single objective bound-constrained numerical optimization problems taken from CEC 2020. Additionally, the JSOA’s performance is tested against several well-known bio-inspired algorithms taken from the literature. The statistical results show that the proposed algorithm outperforms recent literature algorithms and is capable to solve challenging real-world problems with unknown search space.

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Babers, Ramadan, and Aboul Ella Hassanien. "A Nature-Inspired Metaheuristic Cuckoo Search Algorithm for Community Detection in Social Networks." International Journal of Service Science, Management, Engineering, and Technology 8, no. 1 (January 2017): 50–62. http://dx.doi.org/10.4018/ijssmet.2017010104.

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In last few years many approaches have been proposed to detect communities in social networks using diverse ways. Community detection is one of the important researches in social networks and graph analysis. This paper presents a cuckoo search optimization algorithm with Lévy flight for community detection in social networks. Experimental on well-known benchmark data sets demonstrates that the proposed algorithm can define the structure and detect communities of complex networks with high accuracy and quality. In addition, the proposed algorithm is compared with some swarms algorithms including discrete bat algorithm, artificial fish swarm, discrete Krill Herd, ant lion algorithm and lion optimization algorithm and the results show that the proposed algorithm is competitive with these algorithms.

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Carreon-Ortiz, Hector, and Fevrier Valdez. "A new mycorrhized tree optimization nature-inspired algorithm." Soft Computing 26, no. 10 (February 27, 2022): 4797–817. http://dx.doi.org/10.1007/s00500-022-06865-8.

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Haidar, G., S. Ghassempour, and R. Braun. "Nature-Inspired Routing Algorithm for Wireless Sensor Networks." Australian Journal of Electrical and Electronics Engineering 9, no. 3 (January 2012): 327–34. http://dx.doi.org/10.1080/1448837x.2012.11464337.

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Mohammadi-Balani, Abdolkarim, Mahmoud Dehghan Nayeri, Adel Azar, and Mohammadreza Taghizadeh-Yazdi. "Golden eagle optimizer: A nature-inspired metaheuristic algorithm." Computers & Industrial Engineering 152 (February 2021): 107050. http://dx.doi.org/10.1016/j.cie.2020.107050.

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Sharma, Sanjeev, Anupam Shukla, and Ritu Tiwari. "Multi robot area exploration using nature inspired algorithm." Biologically Inspired Cognitive Architectures 18 (October 2016): 80–94. http://dx.doi.org/10.1016/j.bica.2016.09.003.

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30

Lazarowska, Agnieszka. "A Nature Inspired Collision Avoidance Algorithm for Ships." TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation 17, no. 2 (2023): 341–46. http://dx.doi.org/10.12716/1001.17.02.10.

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Templos-Santos, Juan, Omar Aguilar-Mejia, Edgar Peralta-Sanchez, and Raul Sosa-Cortez. "Parameter Tuning of PI Control for Speed Regulation of a PMSM Using Bio-Inspired Algorithms." Algorithms 12, no. 3 (March 4, 2019): 54. http://dx.doi.org/10.3390/a12030054.

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This article focuses on the optimal gain selection for Proportional Integral (PI) controllers comprising a speed control scheme for the Permanent Magnet Synchronous Motor (PMSM). The gains calculation is performed by means of different algorithms inspired by nature, which allows improvement of the system performance in speed regulation tasks. For the tuning of the control parameters, five optimization algorithms are chosen: Bat Algorithm (BA), Biogeography-Based Optimization (BBO), Cuckoo Search Algorithm (CSA), Flower Pollination Algorithm (FPA) and Sine-Cosine Algorithm (SCA). Finally, for purposes of efficiency assessment, two reference speed profiles are introduced, where an acceptable PMSM performance is attained by using the proposed PI controllers tuned by nature inspired algorithms.

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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 constrained mean-variance (CCMV) portfolio problem with entropy constraint was found in the literature. This paper introduces modified firefly algorithm (FA) for the CCMV portfolio model with entropy constraint. Firefly algorithm is one of the latest, very successful swarm intelligence algorithm; however, it exhibits some deficiencies when applied to constrained problems. To overcome lack of exploration power during early iterations, we modified the algorithm and tested it on standard portfolio benchmark data sets used in the literature. Our proposed modified firefly algorithm proved to be better than other state-of-the-art algorithms, while introduction of entropy diversity constraint further improved results.

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Deepika, N., and O. S. Abdul Qadir. "A Study on Nature Inspired Task Scheduling Algorithms in Cloud Environment." Asian Journal of Computer Science and Technology 8, S2 (March 5, 2019): 79–82. http://dx.doi.org/10.51983/ajcst-2019.8.s2.2019.

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Cloud computing is an encouraging paradigm which offers resources to customers on their demand with least cost. Task scheduling is the key difficult in cloud computing which decreases the performance of the system. To develop performance of the system, there is necessity of an effective task-scheduling algorithm. Nature inspired computing is a technique that is inspired by practices detected from nature. These computing techniques led to the growth of algorithms called Nature Inspired Algorithms (NIA). These algorithms are theme of computational intelligence. The persistence of raising such algorithms is to enhance engineering problems. Nature inspired algorithms have enlarged huge popularity in recent years to challenge hard real world (NP hard and NP complete) problems and resolve complex optimization functions whose actual solution doesn’t occur. The paper presents a complete review of 12 nature inspired algorithms. This study offers the researchers with a single platform to analyze the conventional and contemporary nature inspired algorithms in terms of essential input parameters, their key evolutionary strategies and application areas. This study would support the research community to recognize what all algorithms could be observed for big scale global optimization to overwhelm the problem of ‘curse of dimensionality’.

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Chaudhary, Shikha, Saroj Hiranwal, and C. P. Gupta. "Review on Multiobjective Task Scheduling in Cloud Computing using Nature Inspired Algorithms." International Journal of Emerging Research in Management and Technology 6, no. 8 (June 25, 2018): 282. http://dx.doi.org/10.23956/ijermt.v6i8.153.

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In cloud computing huge pool of resources are available and shared through internet. The scheduling is a core technique which determines the performance of a cloud computing system. The goal of scheduling is to allocate task to appropriate machine to achieve one or more QOS. To find the suitable resource among pool of resources to achieve the goal is an NP Complete problem. A new class of algorithm called nature inspired algorithm came into existence to find optimal solution. In this paper we provide a survey as well as a comparative analysis of various existing nature inspired scheduling algorithms which are based on genetic algorithm and ant colony optimization algorithm.

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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 (January 20, 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 machining parameters (peak current, duty factor, wire tension and water pressure) of WEDM are predicted using the six aforementioned metaheuristics. The objective functions of the optimization study are to maximize the material removal rate (MRR) and minimize the wear ratio (WR) and surface roughness (SR). All of the current algorithms have been seen to surpass existing results, thereby indicating their superiority over conventional optimization algorithms.

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Raheem, Khamael Raqim, and Hafedh Ali Shabat. "An Otsu thresholding for images based on a nature-inspired optimization algorithm." Indonesian Journal of Electrical Engineering and Computer Science 31, no. 2 (August 1, 2023): 933. http://dx.doi.org/10.11591/ijeecs.v31.i2.pp933-944.

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Thresholding is a type of image segmentation, where the pixels change to make the image easier to analyze. In bi-level thresholding, the image in grayscale format is transformed into a binary format. The traditional methods for image thresholding may be inefficient in finding the best threshold and take longer computation time. Recently, metaheuristic swarm-based algorithms were applied for optimization in different applications to find optimal solutions with minimum computational time. The proposed work aims to optimize the fitness function obtained by the Otsu algorithm using a metaheuristic swarm-based algorithm called the bat algorithm. As a result, the optimal threshold value for bi-level images in cloud detection was obtained. Also, one of the trajectory-based algorithms called hill climbing was applied to optimize the fitness function taken from the Otsu algorithm. The HYTA dataset was used to evaluate the work, which was later confirmed through testing. The findings of experiments indicated that the developed algorithm is promising and the performance of the metaheuristic population-based algorithm is better than the trajectory-based algorithm in terms of efficiency and computational time for image thresholding.

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Nourin, Asia, Wali Khan Mashwani, Rubi Bilal, Muhammad Sagheer, Habib Shah, Sama Arjika, and Hussain Shah. "An Advanced Amalgam of Nature-Inspired Algorithms for Global Optimization Problems." Mathematical Problems in Engineering 2022 (July 1, 2022): 1–18. http://dx.doi.org/10.1155/2022/7675788.

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Large-scale global optimization problems are ambitious and quite difficult to handle with deterministic methods. The use of stochastic optimization techniques is a good choice for dealing with these problems. Nature-inspired algorithms (NIAs) are stochastic in nature, computer-based, and quite easy to implement due to their population-based nature. The Grey wolf optimizer (GWO) and teaching-learning-based optimization are the most recently developed and well-known NIAs. GWO is based on the preying strategies of grey wolves while TLBO is based on the effect of the influence of a teacher on the output of learners in a class. NIAs are quite often stuck in the local basins of attraction due to the improper balancing of exploration versus exploitation. In this paper, an advanced amalgam of nature-inspired algorithms (ANIA) is developed by employing GWO and TLBO as constituent algorithms. Initially, an equal number of solutions are assigned to both NIAs to perform their search process of population evolution; then, in later iterations, the number of solutions are allocated to each constituent algorithm based on their individual performance and achievements gained by each algorithm in the previous iteration. The performance of an algorithm is determined at the end of iteration by calculating the ratio of total updated solutions to the total assigned solutions in the amalgam. The proposed strategy effectively balanced the exploration versus exploitation dilemma via compelling the parent algorithms to show continuous improvement during the whole course of the optimization process. The performance of the proposed algorithm, ANIA is evaluated on recently designed benchmark functions of large-scale global optimization problems. The approximated results found by the proposed algorithm are promising as compared to state-of-the-art evolutionary algorithms including the GWO and TLBO in terms of diversity and proximity. The proposed ANIA has tackled most of the benchmark functions efficiently in the parlance of evolutionary computing communities.

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Singh, Prem, and Himanshu Chaudhary. "Optimal design of the flywheel using nature inspired optimization algorithms." Open Agriculture 3, no. 1 (November 1, 2018): 490–99. http://dx.doi.org/10.1515/opag-2018-0054.

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Abstract This paper presents the optimal design procedure of a flywheel using a cubic B-spline curve. The flywheel plays a vital role in storing kinetic energy in modern machines. The kinetic energy evaluates the performance of the flywheel. In order to improve the kinetic energy of a flywheel, a shape optimization model of the flywheel, with maximization of kinetic energy, is formulated using a cubic spline curve under the constraints of the mass of flywheel, and the maximum value of Von Mises stresses at all points along the radial direction. The Von Mises stresses at all points are determined using the two-point boundary value differential equation. The control points of the cubic B-spline curve are taken as design variables. Then the formulated problem is solved using particle swarm algorithm (PSO), genetic algorithm (GA), and Jaya algorithm. The proposed approach is applied to the flywheel of an agricultural thresher machine. It is found that the Jaya algorithm gives better results compared to the other algorithms. The optimized shape of the flywheel is simulated using MSC ADAMS software.

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Yang, Xin-She. "Chaos-Enhanced Firefly Algorithm with Automatic Parameter Tuning." International Journal of Swarm Intelligence Research 2, no. 4 (October 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 them to solve a benchmark design problem in engineering. Results obtained by other methods will be compared and analyzed.

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Papakostas, George, John Nolan, and Athanasios Mitropoulos. "Nature-Inspired Optimization Algorithms for the 3D Reconstruction of Porous Media." Algorithms 13, no. 3 (March 16, 2020): 65. http://dx.doi.org/10.3390/a13030065.

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One of the most challenging problems that are still open in the field of materials science is the 3D reconstruction of porous media using information from a single 2D thin image of the original material. Such a reconstruction is only feasible subject to some important assumptions that need to be made as far as the statistical properties of the material are concerned. In this study, the aforementioned problem is investigated as an explicitly formulated optimization problem, with the phase of each porous material point being decided such that the resulting 3D material model shows the same statistical properties as its corresponding 2D version. Based on this problem formulation, herein for the first time, several traditional (genetic algorithms—GAs, particle swarm optimization—PSO, differential evolution—DE), as well as recently proposed (firefly algorithm—FA, artificial bee colony—ABC, gravitational search algorithm—GSA) nature-inspired optimization algorithms were applied to solve the 3D reconstruction problem. These algorithms utilized a newly proposed data representation scheme that decreased the number of unknowns searched by the optimization process. The advantages of addressing the 3D reconstruction of porous media through the application of a parallel heuristic optimization algorithm were clearly defined, while appropriate experiments demonstrating the greater performance of the GA algorithm in almost all the cases by a factor between 5%–84% (porosity accuracy) and 3%–15% (auto-correlation function accuracy) over the PSO, DE, FA, ABC, and GSA algorithms were undertaken. Moreover, this study revealed that statistical functions of a high order need to be incorporated into the reconstruction procedure to increase the reconstruction accuracy.

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Oyelade, Olaide Nathaniel, Absalom El-Shamir Ezugwu, Tehnan I. A. Mohamed, and Laith Abualigah. "Ebola Optimization Search Algorithm: A New Nature-Inspired Metaheuristic Optimization Algorithm." IEEE Access 10 (2022): 16150–77. http://dx.doi.org/10.1109/access.2022.3147821.

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Shandilya, Shishir Kumar, Bong Jun Choi, Ajit Kumar, and Saket Upadhyay. "Modified Firefly Optimization Algorithm-Based IDS for Nature-Inspired Cybersecurity." Processes 11, no. 3 (February 28, 2023): 715. http://dx.doi.org/10.3390/pr11030715.

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The new paradigm of nature-inspired cybersecurity can establish a robust defense by utilizing well-established nature-inspired computing algorithms to analyze networks and act quickly. The existing research focuses primarily on the efficient selection of features for quick and optimized detection rates using firefly and other nature-inspired optimization techniques. However, selecting the most appropriate features may be specific to the network, and a different set of features may work better than the selected one. Therefore, there is a need for a generalized pre-processing step based on the standard network monitoring parameters for the early detection of suspicious nodes before applying feature-based or any other type of monitoring. This paper proposes a modified version of the firefly optimization algorithm to effectively monitor the network by introducing a novel health function for the early detection of suspicious nodes. We implement event management schemes based on the proposed algorithm and optimize the observation priority list based on a genetic evolution algorithm for real-time events in the network. The obtained simulation results demonstrate the effectiveness of the proposed algorithm under various attack scenarios. In addition, the results indicate that the proposed method reduces approximately 60–80% of the number of suspicious nodes while increasing the turnaround time by only approximately 1–2%. The proposed method also focuses specifically on accurate network health monitoring to protect the network proactively.

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Mallick, Abhishek, Atanu Mondal, Somnath Bhattacharjee, and Arijit Roy. "Application of nature inspired optimization algorithms in bioimpedance spectroscopy: simulation and experiment." AIMS Biophysics 10, no. 2 (2023): 132–72. http://dx.doi.org/10.3934/biophy.2023010.

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<abstract> <p>Accurate extraction of Cole parameters for applications in bioimpedance spectroscopy (BIS) is challenging. Precise estimation of Cole parameters from measured bioimpedance data is crucial, since the physiological state of any biological tissue or body is described in terms of Cole parameters. To extract Cole parameters from measured bioimpedance data, the conventional gradient-based non-linear least square (NLS) optimization algorithm is found to be significantly inaccurate. In this work, we have presented a robust methodology to establish an accurate process to estimate Cole parameters and relaxation time from measured BIS data. Six nature inspired algorithms, along with NLS are implemented and studied. Experiments are conducted to obtain BIS data and analysis of variation (ANOVA) is performed. The Cuckoo Search (CS) algorithm achieved a better fitment result and is also able to extract the Cole parameters most accurately among all the algorithms under consideration. The ANOVA result shows that CS algorithm achieved a higher confidence rate. In addition, the CS algorithm requires less sample size compared to other algorithms for distinguishing the change in physical properties of a biological body.</p> </abstract>

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Yetkin, M., and O. Bilginer. "On the application of nature-inspired grey wolf optimizer algorithm in geodesy." Journal of Geodetic Science 10, no. 1 (June 24, 2020): 48–52. http://dx.doi.org/10.1515/jogs-2020-0107.

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AbstractNowadays, solving hard optimization problems using metaheuristic algorithms has attracted bountiful attention. Generally, these algorithms are inspired by natural metaphors. A novel metaheuristic algorithm, namely Grey Wolf Optimization (GWO), might be applied in the solution of geodetic optimization problems. The GWO algorithm is based on the intelligent behaviors of grey wolves and a population based stochastic optimization method. One great advantage of GWO is that there are fewer control parameters to adjust. The algorithm mimics the leadership hierarchy and hunting mechanism of grey wolves in nature. In the present paper, the GWO algorithm is applied in the calibration of an Electronic Distance Measurement (EDM) instrument using the Least Squares (LS) principle for the first time. Furthermore, a robust parameter estimator called the Least Trimmed Absolute Value (LTAV) is applied to a leveling network for the first time. The GWO algorithm is used as a computing tool in the implementation of robust estimation. The results obtained by GWO are compared with the results of the ordinary LS method. The results reveal that the use of GWO may provide efficient results compared to the classical approach.

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Deepak, Malini, and Rabee Rustum. "Review of Latest Advances in Nature-Inspired Algorithms for Optimization of Activated Sludge Processes." Processes 11, no. 1 (December 28, 2022): 77. http://dx.doi.org/10.3390/pr11010077.

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The activated sludge process (ASP) is the most widely used biological wastewater treatment system. Advances in research have led to the adoption of Artificial Intelligence (AI), in particular, Nature-Inspired Algorithm (NIA) techniques such as Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO) to optimize treatment systems. This has aided in reducing the complexity and computational time of ASP modelling. This paper covers the latest NIAs used in ASP and discusses the advantages and limitations of each algorithm compared to more traditional algorithms that have been utilized over the last few decades. Algorithms were assessed based on whether they looked at real/ideal treatment plant (WWTP) data (and efficiency) and whether they outperformed the traditional algorithms in optimizing the ASP. While conventional algorithms such as Genetic Algorithms (GAs), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO) were found to be successfully employed in optimization techniques, newer algorithms such as Whale Optimization Algorithm (WOA), Bat Algorithm (BA), and Intensive Weed Optimization Algorithm (IWO) achieved similar results in the optimization of the ASP, while also having certain unique advantages.

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Hudaib, Amjad A., and Hussam N. Fakhouri. "Supernova Optimizer: A Novel Natural Inspired Meta-Heuristic." Modern Applied Science 12, no. 1 (December 22, 2017): 32. http://dx.doi.org/10.5539/mas.v12n1p32.

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Bio and natural phenomena inspired algorithms and meta-heuristics provide solutions to solve optimization and preliminary convergence problems. It significantly has wide effect that is integrated in many scientific fields. Thereby justifying the relevance development of many applications that relay on optimization algorithms, which allow finding the best solution in the shortest possible time. Therefore it is necessary to further consider and develop new swarm intelligence optimization algorithms. This paper proposes a novel optimization algorithm called supernova optimizer (SO) inspired by the supernova phenomena in nature. SO mimics this natural phenomena aiming to improve the three main features of optimization; exploration, exploitation, and local minima avoidance. The proposed meta-heuristic optimizer has been tested over 20 will known benchmarks functions, the results have been verified by a comparative study with the state of art optimization algorithms Grey Wolf Optimizer (GWO), A Sine Cosine Algorithm for solving optimization problems (SCA), Multi-Verse Optimizer (MVO), Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm (MFO), The Whale Optimization Algorithm (WOA), Polar Particle Swarm Optimizer (PLOARPSO) and with Particle Swarm Optimizer (PSO). The results showed that SO provided very competitive and effective results. It outperforms the best state-of-art algorithms that are compared to on the most of the tested benchmark functions.

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Pleszczyński, Mariusz, Adam Zielonka, and Marcin Woźniak. "Application of Nature-Inspired Algorithms to Computed Tomography with Incomplete Data." Symmetry 14, no. 11 (October 27, 2022): 2256. http://dx.doi.org/10.3390/sym14112256.

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This paper discusses and compares several computed tomography (CT) algorithms capable of dealing with incomplete data. This type of problem has been proposed for a symmetrical grid and symmetrically distributed transmitters and receivers. The use of symmetry significantly speeds up the process of constructing a system of equations that is the foundation of all CT algebraic algorithms. Classic algebraic approaches are effective in incomplete data scenarios, but suffer from low convergence speed. For this reason, we propose the use of nature-inspired algorithms which are proven to be effective in many practical optimization problems from various domains. The efficacy of nature-inspired algorithms strongly depends on the number of parameters they maintain and reproduce, and this number is usually substantial in the case of CT applications. However, taking into account the specificity of the reconstructed object allows to reduce the number of parameters and effectively use heuristic algorithms in the field of CT. This paper compares the efficacy and suitability of three nature-inspired heuristic algorithms: Artificial BeeColony (ABC), Ant Colony Optimization (ACO), and Clonal Selection Algorithm (CSA) in the CT context, showing their advantages and weaknesses. The best algorithm is identified and some ideas of how the remaining methods could be improved so as to better solve CT tasks are presented.

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Salunkhe, Shamal, and Surendra Bhosale. "Nature Inspired Algorithm for Pixel Location Optimization in Video Steganography Using Deep RNN." International Journal on Engineering, Science and Technology 3, no. 2 (January 16, 2022): 146–54. http://dx.doi.org/10.46328/ijonest.67.

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The steganography is applied on text, image, video, and audio files. The steganography is useful for safe and secure data transmission. Video steganography is used to preserve confidential information of security applications. To improve security of the message, pixels locations are optimized using nature inspired algorithm. As conventional algorithms have a low convergence rate a new algorithm is proposed. A New algorithm is developed by combining two model algorithms namely, Water wave optimization (WWO) and Earth worm optimization (EWO) and is renamed as proposed Water-Earth Worm Optimization (WEWO) algorithm. The frames are preprocessed and extracted using Discrete Cosine transform (DCT) and Structured Similarity index (SSIM), respectively, as regular processing. For pixel prediction, the fitness function is obtained from neighborhood entropies in proposed algorithm. In this method, secret message is embedded with two level decomposition of Wavelet Transform (WT). In the proposed work is tested with ‘CAVIAR’ dataset. The Proposed WEWO-Deep RNN algorithm performance is tested with modular noises such as, pepper, salt and pepper noises. The proposed method gives enhanced performance, which is seen with the parameters, Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), and Correlation Coefficient (CC) which defines image quality indices.

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Singh, Dharmpal. "A New Bio-Inspired Social Spider Algorithm." International Journal of Applied Metaheuristic Computing 12, no. 1 (January 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 furnished in details way as compared to different previously proposed swarm intelligence algorithms. Moreover, experimental result has been carried out by modified social spider on series of widely-used benchmark problem with four benchmark algorithms. Furthermore, a modified form of the proposed algorithm has superior performance as compared to other state-of-the-art metaheuristics algorithms.

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Moldovan, Dorin. "Plum Tree Algorithm and Weighted Aggregated Ensembles for Energy Efficiency Estimation." Algorithms 16, no. 3 (March 2, 2023): 134. http://dx.doi.org/10.3390/a16030134.

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This article introduces a novel nature-inspired algorithm called the Plum Tree Algorithm (PTA), which has the biology of the plum trees as its main source of inspiration. The PTA was tested and validated using 24 benchmark objective functions, and it was further applied and compared to the following selection of representative state-of-the-art, nature-inspired algorithms: the Chicken Swarm Optimization (CSO) algorithm, the Particle Swarm Optimization (PSO) algorithm, the Grey Wolf Optimizer (GWO), the Cuckoo Search (CS) algorithm, the Crow Search Algorithm (CSA), and the Horse Optimization Algorithm (HOA). The results obtained with the PTA are comparable to the results obtained by using the other nature-inspired optimization algorithms. The PTA returned the best overall results for the 24 objective functions tested. This article presents the application of the PTA for weight optimization for an ensemble of four machine learning regressors, namely, the Random Forest Regressor (RFR), the Gradient Boosting Regressor (GBR), the AdaBoost Regressor (AdaBoost), and the Extra Trees Regressor (ETR), which are used for the prediction of the heating load and cooling load requirements of buildings, using the Energy Efficiency Dataset from UCI Machine Learning as experimental support. The PTA optimized ensemble-returned results such as those returned by the ensembles optimized with the GWO, the CS, and the CSA.

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