scispace - formally typeset
Search or ask a question

Showing papers on "Metaheuristic published in 2022"


Journal ArticleDOI
TL;DR: The experimental results, along with statistical analysis, reveal the effectiveness of HBA for solving optimization problems with complex search-space, as well as, its superiority in terms of convergence speed and exploration–exploitation balance, as compared to other methods used in this study.

341 citations


Journal ArticleDOI
TL;DR: In this paper , a new metaheuristic optimization algorithm called Honey Badger Algorithm (HBA) is proposed, which is inspired from the intelligent foraging behavior of honey badger, to mathematically develop an efficient search strategy for solving optimization problems.

281 citations


Journal ArticleDOI
TL;DR: Oy et al. as discussed by the authors proposed a new bio-inspired and population-based optimization algorithm named Ebola Optimization Search Algorithm (EOSA) based on the propagation mechanism of the Ebola virus disease.
Abstract: Nature computing has evolved with exciting performance to solve complex real-world combinatorial optimization problems. These problems span across engineering, medical sciences, and sciences generally. The Ebola virus has a propagation strategy that allows individuals in a population to move among susceptible, infected, quarantined, hospitalized, recovered, and dead sub-population groups. Motivated by the effectiveness of this strategy of propagation of the disease, a new bio-inspired and population-based optimization algorithm is proposed. This study presents a novel metaheuristic algorithm named Ebola Optimization Search Algorithm (EOSA) based on the propagation mechanism of the Ebola virus disease. First, we designed an improved SIR model of the disease, namely SEIR-HVQD: Susceptible (S), Exposed (E), Infected (I), Recovered (R), Hospitalized (H), Vaccinated (V), Quarantine (Q), and Death or Dead (D). Secondly, we represented the new model using a mathematical model based on a system of first-order differential equations. A combination of the propagation and mathematical models was adapted for developing the new metaheuristic algorithm. To evaluate the performance and capability of the proposed method in comparison with other optimization methods, two sets of benchmark functions consisting of forty-seven (47) classical and thirty (30) constrained IEEE-CEC benchmark functions were investigated. The results indicate that the performance of the proposed algorithm is competitive with other state-of-the-art optimization methods based on scalability, convergence, and sensitivity analyses. Extensive simulation results show that the EOSA outperforms popular metaheuristic algorithms such as the Particle Swarm Optimization Algorithm (PSO), Genetic Algorithm (GA), and Artificial Bee Colony Algorithm (ABC). Also, the algorithm was applied to address the complex problem of selecting the best combination of convolutional neural network (CNN) hyperparameters in the image classification of digital mammography. Results obtained showed the optimized CNN architecture successfully detected breast cancer from digital images at an accuracy of 96.0%. The source code of EOSA is publicly available at https://github.com/NathanielOy/EOSA_Metaheuristic .

186 citations


Journal ArticleDOI
TL;DR: In this paper , a novel nature-inspired metaheuristic algorithm named as snake optimizer (SO) is proposed to tackle a various set of optimization tasks which imitates the special mating behavior of snakes.
Abstract: In recent years, several metaheuristic algorithms have been introduced in engineering and scientific fields to address real-life optimization problems. In this study, a novel nature-inspired metaheuristics algorithm named as Snake Optimizer (SO) is proposed to tackle a various set of optimization tasks which imitates the special mating behavior of snakes. Each snake (male/female) fights to have the best partner if the existed quantity of food is enough and the temperature is low. This study mathematically mimics and models such foraging and reproduction behaviors and patterns to present a simple and efficient optimization algorithm. To verify the validity and superiority of the proposed method, SO is tested on 29 unconstrained Congress on Evolutionary Computation (CEC) 2017 benchmark functions and four constrained real-world engineering problems. SO is compared with other 9 well-known and newly developed algorithms such as Linear population size reduction-Success-History Adaptation for Differential Evolution (L-SHADE), Ensemble Sinusoidal incorporated with L-SHADE (LSHADE-EpSin), Covariance matrix adaptation evolution strategy (CMAES), Coyote Optimization Algorithm (COA), Moth-flame Optimization, Harris Hawks Optimizer, Thermal Exchange optimization, Grasshopper Optimization Algorithm, and Whale Optimization Algorithm. Experimental results and statistical comparisons prove the effectiveness and efficiency of SO on different landscapes with respect to exploration–exploitation balance and convergence curve speed. The source code is currently available for public from: https://se.mathworks.com/matlabcentral/fileexchange/106465-snake-optimizer

161 citations


Journal ArticleDOI
23 Jan 2022-Sensors
TL;DR: Simulation results and analysis show that Pelican Optimization Algorithm 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.
Abstract: 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.

130 citations


Journal ArticleDOI
TL;DR: In this paper , a new nature-inspired optimization method, named the Golden Jackal Optimization (GJO) algorithm is proposed, which aims to provide an alternative optimization method for solving real-world engineering problems.
Abstract: • Developed Golden Jackal Optimization (GJO) Algorithm as an optimization method. • Tested the performance of proposed algorithm against mathematical and engineering benchmarks. • Compared proposed algorithm with other well-known optimization algorithms. • Conducted statistical analyses. • Demonstrated superiority of proposed algorithm in various conditions. A new nature-inspired optimization method, named the Golden Jackal Optimization (GJO) algorithm is proposed, which aims to provide an alternative optimization method for solving real-world engineering problems. GJO is inspired by the collaborative hunting behaviour of the golden jackals (Canis aureus). The three elementary steps of algorithm are prey searching, enclosing, and pouncing, which are mathematically modelled and applied. The ability of proposed algorithm is assessed, by comparing with different state of the art metaheuristics, on benchmark functions. The proposed algorithm is further tested for solving seven different engineering design problems and introduces a real implementation of the proposed method in the field of electrical engineering. The results of the classical engineering design problems and real implementation verify that the proposed algorithm is appropriate for tackling challenging problems with unidentified search spaces.

114 citations


Journal ArticleDOI
TL;DR: This paper provides a review on the use of machine learning techniques in the design of different elements of meta-heuristics for different purposes including algorithm selection, fitness evaluation, initialization, evolution, parameter setting, and cooperation.

106 citations


Journal ArticleDOI
TL;DR: In this paper , a bio-inspired algorithm inspired by starlings' behaviors during their stunning murmuration named starling murmuration optimizer (SMO) is presented to solve complex and engineering optimization problems as the most appropriate application of metaheuristic algorithms.

104 citations


Journal ArticleDOI
TL;DR: The results indicate that the proposed PDO is effective in estimating optimal solutions for real-world optimization problems with unknown global optima, and shows stronger performance and higher capabilities than the other algorithms.

103 citations


Journal ArticleDOI
TL;DR: Particle swarm optimization (PSO) is one of the most well-regarded swarm-based algorithms in the literature as mentioned in this paper , and many researchers have been modifying it resulting in a large number of PSO variants with either slightly or significantly better performance.
Abstract: Particle swarm optimization (PSO) is one of the most well-regarded swarm-based algorithms in the literature. Although the original PSO has shown good optimization performance, it still severely suffers from premature convergence. As a result, many researchers have been modifying it resulting in a large number of PSO variants with either slightly or significantly better performance. Mainly, the standard PSO has been modified by four main strategies: modification of the PSO controlling parameters, hybridizing PSO with other well-known meta-heuristic algorithms such as genetic algorithm (GA) and differential evolution (DE), cooperation and multi-swarm techniques. This paper attempts to provide a comprehensive review of PSO, including the basic concepts of PSO, binary PSO, neighborhood topologies in PSO, recent and historical PSO variants, remarkable engineering applications of PSO, and its drawbacks. Moreover, this paper reviews recent studies that utilize PSO to solve feature selection problems. Finally, eight potential research directions that can help researchers further enhance the performance of PSO are provided.

99 citations


Journal ArticleDOI
TL;DR: In this article , a reinforcement learning (RL)-based control approach that uses a combination of a deep Q-learning (DQL) algorithm and a metaheuristic Gravitational search algorithm (GSA) is presented.

Journal ArticleDOI
TL;DR: In this paper, a novel reinforcement learning (RL)-based control approach that uses a combination of a deep Q-learning (DQL) algorithm and a metaheuristic Gravitational Search Algorithm (GSA) is employed to initialize the weights and the biases of the Neural Network (NN) involved in DQL in order to avoid the instability.

Journal ArticleDOI
TL;DR: In this article , a hybrid metaheuristic optimization algorithm that combines particle filter (PF) and particle swarm optimization (PSO) algorithms is presented. But this algorithm is not suitable for the optimal tuning of integral-type servo controllers.
Abstract: This article presents a hybrid metaheuristic optimization algorithm that combines particle filter (PF) and particle swarm optimization (PSO) algorithms. The new PF–PSO algorithm consists of two steps: the first generates randomly the particle population;and the second zooms the search domain. An application of this algorithm to the optimal tuning of proportional-integral-fuzzy controllers for the position control of a family of integral-type servo systems is then presented as a second contribution. The reduction in PF–PSO algorithm's cost function allows for reduced energy consumption of the fuzzy control system. A comparison with other metaheuristic algorithms on canonical test functions and experimental results are presented at the end of this article.

Journal ArticleDOI
TL;DR: In this paper , the authors proposed a new metaheuristic optimization algorithm based on ancient war strategy, which is based on the strategic movement of army troops during the war and modeled the war strategy as an optimization process wherein each soldier dynamically moves towards the optimum value.
Abstract: This paper proposes a new metaheuristic optimization algorithm based on ancient war strategy. The proposed War Strategy Optimization (WSO) is based on the strategic movement of army troops during the war. War strategy is modeled as an optimization process wherein each soldier dynamically moves towards the optimum value. The proposed algorithm models two popular war strategies, attack and defense strategies. The positions of soldiers on the battlefield are updated in accordance with the strategy implemented. To improve the algorithm’s convergence and robustness, a novel weight updating mechanism and a weak soldier’s relocation strategy are introduced. The proposed war strategy algorithm achieves good balance of the exploration and exploitation stages. A detailed mathematical model of the algorithm is presented. The efficacy of the proposed algorithm is tested on 50 benchmark functions and four engineering problems. The performance of the algorithm is compared with ten popular metaheuristic algorithms. The experimental results for various optimization problems prove the superiority of the proposed algorithm.


Journal ArticleDOI
TL;DR: Wang et al. as mentioned in this paper proposed a swarm intelligence bioinspired optimization algorithm, called the Dandelion Optimizer (DO), for solving continuous optimization problems, which simulates the process of dandelion seed long distance flight relying on wind, which is divided into three stages.

Journal ArticleDOI
01 May 2022-Heliyon
TL;DR: A thorough review of state-of-the-art and classical strategies for PID controller parameters tuning using metaheuristic algorithms can be found in this article , where the primary objectives of PID control parameters are to achieve minimal overshoot in steady state response and lesser settling time.

Journal ArticleDOI
TL;DR: In this article , a metaheuristic technique called electrostatic discharge algorithm (ESDA) is coupled with an artificial neural network (ANN) to create the proposed hybrid, which is compared to several conventionally trained ANNs to investigate the effect of hybridization.

Journal ArticleDOI
TL;DR: In this paper , a new metaheuristic with blockchain based resource allocation technique (MWBA-RAT) for cyber-twin driven 6G on IoE environment is presented.
Abstract: Rapid advancements of sixth-generation (6G) network and Internet of Everything (IoE) supports numerous emerging services and application. Increasing mobile internet traffic and services, on the other hand, presented a number of challenges that could not be addressed with the current network design. The cybertwin is equipped with a variety of capabilities, including communication assistants, network data loggers, and digital asset owners, to address these difficulties. While spectrum resources are limited, effective resource management and sharing are essential in achieving these requirements. With this motivation, this article presents a new metaheuristic with blockchain based resource allocation technique (MWBA-RAT) for cybertwin driven 6G on IoE environment. The incorporation of the blockchain in 6G enables the network to monitor, manage, and share resources effectively. The proposed MWBA-RAT technique designs a new quasi-oppositional search and rescue optimization (QO-SRO) algorithm for the optimal resource allocation process and this shows the novelty of the work. The QO-SRO algorithm involves the integration of the quasi oppositional based learning concept with the traditional SRO algorithm to improve its convergence rate. A wide range of experiments are performed to highlight the enhanced outcomes of the MWBA-RAT technique.

Journal ArticleDOI
TL;DR: A comprehensive survey of the tree seed algorithm (TSA) and its applications in a wide range of different fields is performed and covers all the TSA empirical literature in hybridization, Improved, Variants and Optimization.

Journal ArticleDOI
TL;DR: The most outstanding recent metaheuristic feature selection algorithms of the last two decades in terms of their performance in exploration/exploitation operators, selection methods, transfer functions, fitness value evaluations, and parameter setting techniques are surveyed in this paper .

Journal ArticleDOI
TL;DR: Zhang et al. as discussed by the authors proposed a swarm-based metaheuristic algorithm inspired from the behaviors of beluga whales, called Beluga Whale Optimization (BWO), to solve optimization problem.
Abstract: In this paper, a novel swarm-based metaheuristic algorithm inspired from the behaviors of beluga whales, called beluga whale optimization (BWO), is presented to solve optimization problem. Three phases of exploration, exploitation and whale fall are established in BWO, corresponding to the behaviors of pair swim, prey, and whale fall, respectively. The balance factor and probability of whale fall in BWO are self-adaptive which play significant roles to control the ability of exploration and exploitation. Besides, the Levy flight is introduced to enhance the global convergence in the exploitation phase. The effectiveness of the proposed BWO is tested using 30 benchmark functions, with qualitative, quantitative and scalability analysis, and the statistical results are compared with 15 other metaheuristic algorithms. According to the results and discussion, BWO is a competitive algorithm in solving unimodal and multimodal optimization problems, and the overall rank of BWO is the first in the scalability analysis of benchmark functions among compared metaheuristic algorithms through the Friedman ranking test. Finally, four engineering problems demonstrate the merits and potential of BWO in solving complex real-world optimization problems. The source code of BWO is currently available to public: https://ww2.mathworks.cn/matlabcentral/fileexchange/112830-beluga-whale-optimization-bwo/ . • A novel metaheuristic algorithm named as Beluga Whale Optimization (BWO) is proposed. • The behaviors of swim, prey and whale fall are designed on BWO algorithm. • The BWO is tested on 30 well-known benchmark functions and 4 engineering problems. • The BWO is compared with 15 well-known metaheuristic algorithms. • The BWO outperforms comparing algorithms in benchmark functions, especially for scalability of dimension.

Journal ArticleDOI
TL;DR: In this paper , a novel meta-heuristic algorithm inspired by the social life and hierarchy of wild mountain gazelles is proposed, which is evaluated and tested using several test beds and case studies.
Abstract: • A novel meta-heuristic is proposed inspired by the social life and hierarchy of wild mountain gazelles. • Gazelles' hierarchical and social life is formulated mathematically and used to develop an optimization algorithm. • The proposed algorithm is evaluated and tested using several test beds and case studies. • The results demonstrate that the proposed algorithm performs better than the comparable algorithms. The Mountain Gazelle Optimizer (MGO), a novel meta-heuristic algorithm inspired by the social life and hierarchy of wild mountain gazelles, is suggested in this paper. In this algorithm, gazelles' hierarchical and social life is formulated mathematically and used to develop an optimization algorithm. The MGO algorithm is evaluated and tested using Fifty-two standard benchmark functions and seven different engineering problems. It is compared with nine other powerful meta-heuristic algorithms to validate the result. The significant differences between the comparative algorithms are demonstrated using Wilcoxon's rank-sum and Friedman's tests. Numerous experiments have shown that the MGO performs better than the comparable algorithms on utmost benchmark functions. In addition, according to the tests performed, the MGO maintains its search capabilities and shows good performance even when increasing the dimensions of optimization problems. The source codes of the MGO algorithm are publicly available at https://www.mathworks.com/matlabcentral/fileexchange/118680-mountain-gazelle-optimizer .

Journal ArticleDOI
TL;DR: In this paper , a new stochastic optimization algorithm is introduced, called Driving Training-Based Optimization (DTBO), which mimics the human activity of driving training and is mathematically modeled in three phases: (1) training by the driving instructor, (2) patterning of students from instructor skills, and (3) practice.
Abstract: In this paper, a new stochastic optimization algorithm is introduced, called Driving Training-Based Optimization (DTBO), which mimics the human activity of driving training. The fundamental inspiration behind the DTBO design is the learning process to drive in the driving school and the training of the driving instructor. DTBO is mathematically modeled in three phases: (1) training by the driving instructor, (2) patterning of students from instructor skills, and (3) practice. The performance of DTBO in optimization is evaluated on a set of 53 standard objective functions of unimodal, high-dimensional multimodal, fixed-dimensional multimodal, and IEEE CEC2017 test functions types. The optimization results show that DTBO has been able to provide appropriate solutions to optimization problems by maintaining a proper balance between exploration and exploitation. The performance quality of DTBO is compared with the results of 11 well-known algorithms. The simulation results show that DTBO performs better compared to 11 competitor algorithms and is more efficient in optimization applications.

Journal ArticleDOI
TL;DR: The SA has the best compromise between robustness, accuracy, and rapidity, and is found to be the best option to solve the sizing problem, and the FPA is the most advantageous in case the execution time is not crucial for the optimization.

Journal ArticleDOI
TL;DR: In this paper , the authors proposed an enhanced firefly algorithm adapted for tackling workflow scheduling challenges in a cloud-edge environment, which overcomes observed deficiencies of original firefly metaheuristic by incorporating genetic operators and quasi-reflection-based learning procedure.
Abstract: Edge computing is a novel technology, which is closely related to the concept of Internet of Things. This technology brings computing resources closer to the location where they are consumed by end-users-to the edge of the cloud. In this way, response time is shortened and lower network bandwidth is utilized. Workflow scheduling must be addressed to accomplish these goals. In this paper, we propose an enhanced firefly algorithm adapted for tackling workflow scheduling challenges in a cloud-edge environment. Our proposed approach overcomes observed deficiencies of original firefly metaheuristics by incorporating genetic operators and quasi-reflection-based learning procedure. First, we have validated the proposed improved algorithm on 10 modern standard benchmark instances and compared its performance with original and other improved state-of-the-art metaheuristics. Secondly, we have performed simulations for a workflow scheduling problem with two objectives-cost and makespan. We performed comparative analysis with other state-of-the-art approaches that were tested under the same experimental conditions. Algorithm proposed in this paper exhibits significant enhancements over the original firefly algorithm and other outstanding metaheuristics in terms of convergence speed and results' quality. Based on the output of conducted simulations, the proposed improved firefly algorithm obtains prominent results and managed to establish improvement in solving workflow scheduling in cloud-edge by reducing makespan and cost compared to other approaches.

Journal ArticleDOI
TL;DR: In this article, a collaborative neuro-dynamic optimization approach is presented for cardinality-constrained portfolio selection, where the expected return and investment risk in the Markowitz framework are scalarized as a weighted Chebyshev function and the cardinality constraints are equivalently represented using introduced binary variables as an upper bound.

Journal ArticleDOI
TL;DR: An alternative task scheduler approach for organizing IoT application tasks over the CCE, using a modified Manta ray foraging optimization (MRFO) and the salp swarm algorithm (SSA), is proposed to handle the problem of scheduling IoT tasks in cloud computing.
Abstract: The usage of cloud services is growing exponentially with the recent advancement of Internet of Things (IoT)-based applications. Advanced scheduling approaches are needed to successfully meet the application demands while harnessing cloud computing’s potential effectively to schedule the IoT services onto cloud resources optimally. This article proposes an alternative task scheduler approach for organizing IoT application tasks over the CCE. In particular, a novel hybrid swarm intelligence method, using a modified Manta ray foraging optimization (MRFO) and the salp swarm algorithm (SSA), is proposed to handle the problem of scheduling IoT tasks in cloud computing. This proposed method, called MRFOSSA, depends on using SSA to improve the local search ability of MRFO that typically enhances the rate of convergence towards the global solution. To validate the developed MRFOSSA, a set of experimental series is performed using different real-world and synthetic datasets with variant sizes. The performance of MRFOSSA is tested and compared with other metaheuristic techniques. Experiment results show the superiority of MRFOSSA over its competitors in terms of performance measures, such as makespan time and cloud throughput.

Journal ArticleDOI
TL;DR: In this article , two improved PSO algorithms are proposed to enhance the convergence rate with global optimal results during the structural reliability analysis using a hybrid PSO-based harmony search algorithm (PSO-HS) and enhanced PSO (EPSO).

Book ChapterDOI
01 Jan 2022
TL;DR: An improved version of swarm intelligence and monarch butterfly optimization algorithm for training the feed-forward artificial neural network is devised and outperforms other state-of-the-art algorithms that are shown in the recent outstanding computer science literature.
Abstract: Artificial neural networks, especially deep neural networks, are the promising and current research domain as they showed great potential in classification and regression tasks. The process of training artificial neural network (weight optimization), as an NP-hard challenge, is typically performed by back-propagation algorithms such as stochastic gradient descent. However, these types of algorithms are susceptible to trapping the local optimum. Recent studies show that, the metaheuristics-based approaches like swarm intelligence can be efficiently utilized in training the artificial neural network. This paper presents an improved version of swarm intelligence and monarch butterfly optimization algorithm for training the feed-forward artificial neural network. Since the basic monarch butterfly optimization suffers from some deficiencies, improved implementation, that enhances exploration ability and intensification–diversification balance, is devised. Proposed method is validated against 8 well-known classification datasets and compared to similar approaches that were tested within the same environment and simulation setup. Obtained results indicate that, the method proposed in this work outperforms other state-of-the-art algorithms that are shown in the recent outstanding computer science literature.