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In the data mining and machine learning (ML) discipline, feature selection problem is considered among many researchers in the recent times. Feature selection process targets to minimize feature set number and maximize performance accuracy by identifying optimal features. Multiple objectives are considered while identifying the optimal feature hence multi-objective metaheuristic optimization algorithms (MOMOAs) are applied. In this study, literature review is performed MOMOAs-for solving wrapper based feature selection problem (WFS). The literature review for solving WFS problem and discuss the challenges faced by the researchers in solving the feature selection problem. The literature review is performed on all relevant studies published in the last 12 years [2009-2022]. A detailed overview of the feature selection preliminaries, MOMOAs-WFS, role of the classifier in feature selection problem are presented. The outcome of this literature review is to highlight the existing works related to WFS problem using MOMOAs. Finally, the research areas for improvement are identified and emphasized for the scientists to survey in the field of MOMOAs.

The increased penetration of distributed energy resources (DERs) technologies to residential users has fostered the need for DERs integration and control methods in the secondary distribution networks (SDN). In order to reap the potential advantages of DERs and achieve their inclusion in the electrical power system while avoiding their negative impacts, the DERs should be optimally placed and sized. Considering the nature of electrical networks and DER operations, the DERs placement is a nondeterministic polynomial hard (NP-hard) optimization problem. Metaheuristic algorithms are efficient for solving DER placement problems. Metaheuristic algorithms for DER placement in SDN involve high computational effort, theoretical convergence assumptions that cannot be satisfied in the real world and dependence on parameter settings. Therefore, this study proposes a DER placement algorithm that employs a cloud-based model symbiotic organism search algorithm (CMSOS). The CMSOS is attributed to simple implementation and computation, good convergence, and parameter independence. The electrical network segment taken for Tanzania’s electrical distribution network was used for testing the algorithms, considering power loss and voltage deviations. Results show that using DERs in the proposed locations reduces power loss by 89.3%. The convergence profile shows that the proposed CMSOS-based algorithm converges faster than the conventional symbiotic organism search algorithm (SOS). Keywords: Metaheuristic Algorithms, Symbiotic Organism Search, DER Placements, Radial Distribution Network, Cloud-based model

The fast improvements in wireless technology and embedded systems have sparked a renewed interest in collaborative beamforming (CB) approach used in wireless sensor networks (WSNs). Despite the fact that studies on distributed and CB have been conducted for more than 10 years, CB was previously deemed unworkable because of its extreme complexity and difficult-to-attain criteria. It just got well-known in the last several years when cheaply accessible compact wireless communication electronic sensors with high processing capability emerged. These factors contributed as the motivation for this paper's research overview CB in WSNs. We provide the classifications of the static and mobile WSN which is based on the sensor node optimization technique. This paper reviews the metaheuristic algorithms proposed by previous study for beampattern and energy consumption optimization. Finally, this paper also presents a summary analysis of the previous studies in terms of beampattern characteristic and properties, energy consumption and stability.

Metasezgisel yöntemler genellikle doğadan ilham alınarak oluşturulmuş algoritmalardır. Bu yöntemler özellikle karmaşık problemlerin çözümünde oldukça başarılı sonuçlar üretmektedir. Önerilen yöntemlerin performansları, uygulanan probleme göre değişiklik göstermektedir. Bu çalışmada son dönemlerde ortaya çıkmış ve popüler olan Harris Şahin Optimizasyon Algoritması, Serçe Arama Algoritması, Çoklu Evren Optimizasyonu, Deniz Avcıları Algoritması ve Coot Optimizasyon Algoritması detaylı bir şekilde incelenmiştir. Bu algoritmalar 23 standart kalite testi fonksiyonlarında analiz edilmiştir. Analiz edilen fonksiyonlar tek modlu kalite testi fonksiyonları, çok modlu kalite testi fonksiyonları, karmaşık boyutlu çok modlu kalite testi fonksiyonlarından oluşmaktadır., Metaheuristic methods are generally algorithms inspired by nature. These methods produce very successful results especially in solving complex problems. The performances of the proposed methods vary according to the applied problem. In this study, the recently emerged and popular Harris Hawk Optimization Algorithm, Sparrow Search Algorithm, Multi-verse Optimization, Marine Predators Algorithm and Coot Optimization Algorithm are examined in detail. These algorithms were analyzed in 23 standard benchmark functions. The analyzed functions consist of unimodal benchmark functions, multimodal benchmark functions, fixed dimension multimodal benchmark functions.

Doğal gaz talep tahmini, özellikle enerji tüketimi yüksek ülke ekonomilerinin karar vericileri, sanayi sektörü ve doğal gaz piyasasındaki tüm oyuncular için büyük önem taşımaktadır. Bu çalışma, meteorolojik parametrelere göre Türkiye'nin aylık doğal gaz talep tahmini modelini sunmayı amaçlamaktadır. Çalışmada Yapay Arı Kolonisi Algoritması (ABC), Yüklü Sistem Arama Algoritması (CSS), Karga Arama Algoritması (CSA) ve Harmoni Arama Algoritması (HSA) dört güncel metasezgisel algoritma ile oluşturulan modeller karşılaştırılmıştır. Araştırmada lineer, üstel (exponential) ve ikinci dereceden (quadratic) olmak üzere üç matematiksel model geliştirilmiş ve modellerin performansları altı farklı global hata ölçüm metrikleri (AE, MAE, R2, MAPE, RMS, MARNE) ile değerlendirilmiştir. Çalışmada ortalama sıcaklık, basınç, nem, rüzgar ve yağış meteorolojik veriler girdi parametreleri olarak kullanılmıştır. 2010-2017 yılları arasındaki veriler eğitim verileri, 2018-2020 yılları arasındaki veriler ise test verisi olarak uygulanmıştır. Doğal gaz talep tahmini eğitim veri seti için en başarılı tahmin eden model CSS algoritmasının quadratic modeliyken, test verilerinde ise en başarılı tahmin ABC algoritmasının quadratic modelidir., Natural gas demand forecasting is of great importance especially for decision-makers of national economies with high energy consumption, the industrial sector, and all players in the natural gas market, in particular. This study aims to present Turkey's monthly natural gas demand forecast model according to meteorological parameters. In the study, models were created with four current metaheuristic algorithms of Artificial Bee Colony Algorithm (ABC), Charged System Search Algorithm (CSS), Crow Search Algorithm (CSA), and Harmony Search Algorithm (HSA) were compared. In the research, three mathematical models, linear, exponential, and quadratic, were developed and the performances of the models were evaluated with six different global error measurement metrics (AE, MAE, R2, MAPE, RMS, MARNE). In the study, average temperature, pressure, humidity, wind, and precipitation meteorological data were used as input parameters. The data between 2010-2017 was applied as training data, and the data between 2018-2020 was applied as test data. The most successful forecasting model for the natural gas demand forecast training data set is the quadratic model of the CSS algorithm, while the most successful forecasting model for the test data is the quadratic model of the ABC algorithm.

In recent years, grey models based on fractional-order accumulation and/or derivatives have attracted considerable research interest because they offer better performance in handling limited samples with uncertainty than integer-order grey models; however, there remains room for improvement. This paper considers a more flexible and general structure for the fractional grey model by incorporating a generalized fractional-order derivative (GFOD) that complies by memory effects, resulting in the development of a generalized fractional grey model (denoted as GFGM(1,1)). Specifically, we comprehensively analyse the modelling mechanism of the proposed GFGM(1,1) model, involving model parameter estimation and time response function derivation, and discuss the link between the proposed approach and existing special cases. Then, to further improve the efficacy of the proposed approach, four mainstream metaheuristic algorithms are employed to ascertain the orders of fractional accumulation and derivatives. Finally, we carry out a series of simulation studies and a real-world application case to demonstrate the applicability and advantage of the our approach. The numerical results show that GFGM(1,1) outperforms other benchmarks, and some significant insights are obtained from the numerical experiments.

International audience; The purpose of this work was to compare the performance of 7 meta-heuristics algorithms namely: Dragonfly (DA), Ant Lion (ALO), Grey Wolf (GWO), Artificial Bee Colony (ABC), Particle Swarm (PSO), Whale (WAO), and a hybrid Particle Swarm with Grey Wolf (HPSOGWO) optimizers in terms of fine-tuning hyper-parameters of a hybrid quantitative structure property relationships (QSPR)-support vector regression (SVR) for the prediction of molar fraction solubilities of drug compounds in supercritical carbon dioxide (SC-CO2). A dataset of 168 drug compounds, 13 inputs, and 4490 experimental data points was used to achieve the goal. All 7 models were statistically and graphically approved while the HPSOGWO-SVR was found to over-perform with an average absolute relative deviation (AARD) of 0.706% and an AIC of -14,434,249. The model was subjected to an external test (validation) using 160 experimental data points that were not used in the training and the test set. The overall results proved that the obtained model has good predictivity ability and robustness.

One of the greatest techniques, inductive coupling is frequently utilized in the biomedical sector for wireless energy transfer to implants. The aim of this article is to develop and analyze the effect of inductor geometrical characteristics, distance between transmitter (TX) and receiver (RX) and also the operating frequency on the wireless power transfer system, using grey wolf optimizer-based cuckoo search (GWO-CS) algorithm. Power transfer efficiency (PTE), power provided to load, and other critical components must all be improved or maximized and miniaturaze the microsystem proposed. The invention, design, and optimization of coils square spirals in a wireless energy transfer system using a resonant inductive link are the emphasis of this paper. The GWO-CS approach is evaluated to existing methods, demonstrated by simulations and to demonstrate the effectiveness of the suggested strategy.

Training an Artificial Neural Network (ANN) algorithm is not trivial, which requires optimizing a set of weights and biases that increase dramatically with the increasing capacity of the neural network resulting in such hard optimization problems. Essentially, over recent decades, stochastic search algorithms have shown remarkable abilities for addressing hard optimization problems. On the other hand, pragmatically, abundant real-world problems suffer from the imbalance problem, where the distribution of data varies considerably among classes resulting in more training biases and variances which degrades the performance of the learning algorithm. This paper introduces three stochastic and metaheuristic algorithms for training the Multilayer Perceptron (MLP) neural network to solve the problem of imbalanced classifications. The utilized algorithms are the Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and the Salp Swarm Algorithm (SSA). The proposed GWO-MLP, PSO-MLP, and SSA-MLP are trained based on different objective functions; accuracy, f1-score, and g-mean. Whereas, it is evaluated based on 10 benchmark imbalanced datasets. The results show an advantage for f1-score, and g-mean fitness functions over the accuracy when the datasets are imbalanced.

Task scheduling is one of the major issues in cloud computing environment. Efficient task scheduling is substantial to attain cost-effective execution and improve resource utilization. The task scheduling problem is classified to be a nondeterministic polynomial time (NP)-hard problem. This feature attracts researchers to utilize nature inspired metaheuristic algorithms. Initializing searching solutions randomly is one of the key features in such optimization algorithms. However, assisting metaheuristic algorithms with effective initialized solutions can significantly improve its performance. In this paper, an improved initialization of particle swarm optimization (PSO) using heuristic algorithms is proposed. Longest job to fastest processor (LJFP) and minimum completion time (MCT) algorithms are used to initialize the PSO. The performance of the proposed LJFP-PSO and MCT-PSO algorithms are evaluated in minimizing the makespan, total execution time, degree of imbalance, and total energy consumption metrices. Moreover, the performance of the proposed algorithms is compared with recent task scheduling methods. Simulation results revealed the effectiveness and superiority of the proposed LJFP-PSO and MCT-PSO compared to the conventional PSO and comparative algorithms.

Classification is used to categorize data and produce decisions for several domains. To improve the accuracy of classification, researchers have tended to hybridize the neural network with other metaheuristic algorithms in order to better exploit and explore the search space and thereby solve many different classification problems in an effective manner. The hybridization of algorithms is now commonplace and has resulted in the creation of novel methods that are more effective in comparison with those that employ a sole algorithm. Therefore, in this paper, a hybridization approach is employed to utilize the African buffalo optimization (ABO) algorithm as an optimizer to adjust the weights of the probabilistic neural network (PNN). The effectiveness of the proposed (ABO-PNN) method is investigated by applying it to several different classification problems. The efficiency of the ABO algorithm is assessed based on the PNN training results produced and its performance is compared with that of different types of optimization algorithm. The performance of the proposed algorithm in terms of classification accuracy is tested on 11 benchmark datasets. The results show that the ABO is better than the firefly algorithm (FA) in terms of both classification accuracy and convergence speed.

Metaheuristic optimization techniques have been used to solve engineering problems with an increasing speed for the last 30 years. Most of these algorithms have been developed by imitating a process in nature. In this study, the ant colony algorithm inspired by the natural life of ants is discussed. The ant colony algorithm requires some parameters to perform an optimization, as in other meta-heuristic algorithms. The aim of this study is to examine the effect the values of the parameters used in the ant colony algorithm on the results. For this purpose, as an exemplary problem, a study was carried out on the optimization of truss systems, one of the constrained problems frequently discussed in the literature. Appropriate values of optimum design parameters such as number of ants, pheromone update coefficient and penalty coefficient were investigated using the coded computer program. As a result of the study, the effect of the relevant parameters on the result was determined and the points to be considered in the selection of these parameters were specified.

In many applications, selecting the optimal features is a difficult task. Numerous In optimization problems, e.g., feature selection (FS) problem, have been solved using optimization algorithms. In this paper, the most discriminating features were chosen using a new chaotic gradient-based optimizer (CGBO) that combines chaotic maps with searching iterations of the gradient-based optimizer (GBO). Ten chaotic maps were utilised to update the parameters, eliminate a local optimum and premature convergence, accelerate convergence, and enhance the efficiency of GBO. A classifier was used in FS approaches to determine the best subset of characteristics. The proposed CGBO uses the k-nearest neighbor as an objective function for the classification process in FS. Ten datasets from the UCI machine learning repository were used to validate CGBO. In an experiment, CGBO outperformed five other metaheuristic algorithms: particle swarm optimization(PSO), moth flame optimizer(MFO), sine cosine algorithm(SCA), salp swarm algorithm(SSA), and GBO. The results demonstrated the capability of CGBO to find and select the optimal feature subset, which maximized the classification performance and minimized the number of features selected efficiently compared with other metaheuristic algorithms.

In this study, the modification of the Deb feasibility method is considered to solve the constrained optimization problems. In the developed modified Deb feasibility constraint method, the third rule in its procedure was revised in order to increase the performance of the Deb feasibility constraint handling method. The innovation in the method is based on generating a new individual by using both possible solutions that violate the constraints in the method used for solving the problem. In detail, discussions were given about the application and usefulness of six constrained handling techniques. Furthermore, genetic algorithm, particle swarm optimization, Harris hawks optimization, whale optimization algorithm, grey wolf optimization and sine cosine algorithms were applied to both various benchmark functions and also different engineering application problems such as pressure vessel design, welded beam design, speed reducer design and active filter design. Overall the experimental results show that modified Deb feasibility constraint handling technique is more robust and efficient than Deb feasibility technique and most of the other constraint handling techniques.

This paper presents a new optimization procedure to consider uncertainty in complex optimization problems, where conventional mathematical programming strategies are not efficient. This methodology consists in using evolutionary algorithms based on metaheuristic optimization tools. The proposed sequence involves the generation of random values to consider the stochastic behavior of uncertain parameters in the mathematical model. Likewise, a subcode was developed in visual basic for applications to manipulate the decision variables generated by the evolutionary algorithm and the post evaluation of the performance of the objective functions. The used metaheuristic optimization algorithm was the improved multi-objective differential evolution optimization algorithm. To prove the efficiency of the proposed optimization strategy, three case studies are analyzed, which involve the solution of single-objective and bi-objective optimization problems. This optimization procedure offers attractive solutions to the specified goals.

Novel data-intelligence models developed through hybridization of an adaptive neuro-fuzzy inference system (ANFIS) with different metaheuristic algorithms, namely grey wolf optimizer (GWO), particl...

Estimating total precipitable water vapour (TPWV) from advanced microwave scanning radiometer 2 (AMSR2) measurements with reasonable accuracy is challenging. Therefore, this study aims to improve t...

Bat algorithm (BA) is a population-based metaheuristic algorithm inspired by echolocation behavior of bat. After the development of BA in 2010, it becomes the attention of researchers from various domains. It has been successfully applied in various real-life engineering problems. This paper presents a comprehensive review of BA. The biological inspiration and working of BA are deliberated. The variants of BA namely improved, hybrid, levy flight, chaotic, binary, and multiobjective are analyzed in detail. The applications of BA in different research domains are investigated. The current challenges and future research directions of BA are discussed. Besides highlighting the new developments in structure of BA, this study can be act as a baseline for young researchers to enhance the performance of their metaheuristic algorithms on adapting the BA. This study will encourage the young researchers and scientists to use BA in their research problems.

This paper deals with a simple but efficient method for detection of deadly malignant melanoma with optimized hand-crafted feature sets selected by three alternative metaheuristic algorithms, namely Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) and Simulated Annealing (SA). Total 1898 number of features relating to lesion shapes, colors and textures are extracted from each of the 170 non-dermoscopy camera images of the popular MED-NODE dataset. This large feature set is then optimized and the number of features is reduced to up-to the range of single digit using metaheuristic algorithms as feature selector. Two well-known supervised classifiers, i.e. Support Vector Machine (SVM) and Artificial Neural Network (ANN) are used to classify malignant and benign lesions. The best classification accuracy result found by this method is 87.69% with only 7 features selected by PSO using ANN classifier which is far better than the results found in the literature so far.

Recently, studies have focused on optimization as a method to reach the finest conditions for metal forming processes. This study tests various optimization techniques to determine the optimum conditions for single point incremental forming (SPIF). SPIF is a die-less forming process that depends on moving a tool along a path designed for a specific feature. As it involves various parameters, optimization based on experimental studies would be costly, hence a finite element model (FE-model) for the SPIF process is developed and validated through experimental results. In the second phase, statistical analyses based on the response surface method (RSM) are conducted. The optimum conditions are determined using the desirability optimization method, in addition to two metaheuristic optimization algorithms, namely genetic algorithm (GA) and particle swarm optimization (PSO). The results of all optimization techniques are compared to each other and a confirmation test using the FE-model is subsequently performed.

The butterfly optimization algorithm is one of the metaheuristic algorithms that models the foraging behavior of butterflies. It appears in the literature that it has been used successfully in the solution of many problems. Control parameters and dimension of problem directly affect the performance of a metaheuristic algorithm. When possible problems are evaluated, some may be low-dimensional, while others may be high-dimensional. At the same time, it is expected that metaheuristic algorithms are successful on high-dimensional problems as well. In this study, the performance of the butterfly optimization algorithm is evaluated in solving high-dimensional numerical optimization problems. The performance of butterfly optimization algorithm is compared with artificial bee colony algorithm, harmony search and flower pollination algorithm. Wilcoxon signed-rank test is used to analyze the significance of the results. The results show that the butterfly optimization algorithm is generally more successful than other algorithms in solving high-dimensional numerical optimization problems., Kelebek optimizasyon algoritması, kelebeklerin yiyecek arama davranışını modelleyen güncel meta-sezgisel algoritmalardan biridir. Pek çok problemin çözümünde başarılı bir şekilde kullanıldığı literatürde görülmektedir. Bir meta sezgisel algoritmanın başarısını, kontrol parametreleri ve problem boyutu doğrudan etkilemektedir. Muhtemel problemler değerlendirildiğinde, bazıları düşük boyutlu iken, bazıları yüksek boyutlu olabilmektedir. Bununla birlikte, meta-sezgisel algoritmaların yüksek boyutlu problemler üzerinde de başarılı olması beklenmektedir. Bu çalışmada, yüksek boyutlu nümerik optimizasyon problemlerinin çözümünde kelebek optimizasyon algoritmasının performansı değerlendirilmiştir. Kelebek optimizasyon algoritması; yapay arı koloni algoritması, armoni arama algoritması ve çiçek tozlaşma algoritması ile karşılaştırılmıştır. Sonuçların anlamlılığının analizi için Wilcoxon işaretli sıralar testi uygulanmıştır. Sonuçlar, çok boyutlu nümerik optimizasyon problemlerinin çözümünde, kelebek optimizasyon algoritmasının diğer algoritmalardan genel olarak daha başarılı olduğunu göstermiştir.

Microscopic Image segmentation has a crucial role in detecting and diagnosing numerous critical diseases like Alzheimer’s disease, Kidney disease, Cancer, many infectious diseases, etc. Precise segmentation of hippocampus microscopic images is a prerequisite for analyzing and interpreting the brain tissues. A few metaheuristic-based multilevel image segmentation methods are found in the literature for the same. In this work, an enhanced firefly algorithm-based image segmentation method has been proposed to achieve a good quality segmentation. The proposed algorithm utilizes the classical firefly algorithm’s movement operation along with the concept of quantum superposition and quantum update operation. In this algorithm, the movements of quantum fireflies have been modeled based on two strategies: firstly, the less bright fireflies move towards the comparatively brighter ones and secondly, quantum fireflies are updated according to the global optimum by the quantum update operation. This global steered Quantum Inspired Firefly Algorithm (QIFA) has been proposed and used for the multilevel hippocampus image segmentation considering correlation and structural similarity index as objective functions. In order to validate the quality of segmentation, the F-score values with respect to the segmented images have been reported. The proposed algorithm’s performance has been compared with seven other metaheuristic algorithms. The experimental results establish that the proposed algorithm is effective in producing good quality segmentation of hippocampus images.

Maintenance and Rehabilitation (M&R) scheduling is one of the vital aspects of a pavement management system (PMS). This study aims to establish accurate M&R plans for a large-scale pavement network. To this intent, parameters affecting pavement deterioration were identified from the literature, then Random Forest Regression was employed to determine the effective features for pavement deterioration modelling. An accurate pavement deterioration function was generated by applying significant features. The most robust metaheuristic and evolutionary algorithms were selected and adjusted to solve the M&R scheduling optimisation problem, including the Water Cycle Algorithm (WCA), Arithmetic Optimisation Algorithm (AOA), Differential Evolutionary (DE), Ant Colony Optimisation (ACO), Particle Swarm Optimisation (PSO), and Genetic Algorithm (GA). The performance of the mentioned algorithms was compared to help researchers and decision-makers to select the appropriate algorithm for M&R scheduling optimisation. WCA and AOA showed to have the best performance among the adapted algorithms. Compared to AOA, DE, ACO, PSO, and GA, WCA's objective function was calculated to be 45%, 74%, 74%, 77% and 83% less, while its M&R cost was cheaper by 13%, 16%, 27%, 19%, and 18%, respectively.

Flow-shop scheduling is an extremely popular optimization model in industrial production, where each job must be scheduled on a series of machines following an identical process. The optimal criter...

Purpose Efficient resource utilization in wireless sensor networks is an important issue. Clustering structure has an important effect on the efficient use of energy, which is one of the most critical resources. However, it is extremely vital to choose efficient and suitable cluster head (CH) elements in these structures to harness their benefits. Selecting appropriate CHs and finding optimal coefficients for each parameter of a relevant fitness function in CHs election is a non-deterministic polynomial-time (NP-hard) problem that requires additional processing. Therefore, the purpose of this paper is to propose efficient solutions to achieve the main goal by addressing the related issues. Design/methodology/approach This paper draws inspiration from three metaheuristic-based algorithms; gray wolf optimizer (GWO), incremental GWO and expanded GWO. These methods perform various complex processes very efficiently and much faster. They consist of cluster setup and data transmission phases. The first phase focuses on clusters formation and CHs election, and the second phase tries to find routes for data transmission. The CH selection is obtained using a new fitness function. This function focuses on four parameters, i.e. energy of each node, energy of its neighbors, number of neighbors and its distance from the base station. Findings The results obtained from the proposed methods have been compared with HEEL, EESTDC, iABC and NR-LEACH algorithms and are found to be successful using various analysis parameters. Particularly, I-HEELEx-GWO method has provided the best results. Originality/value This paper proposes three new methods to elect optimal CH that prolong the networks lifetime, save energy, improve overhead along with packet delivery ratio.

Metaheuristic algorithms are widely used in various fields of optimization engineering. These algorithms have become popular because of their ability to explore and exploit solutions in various problem areas. The Spotted Hyena Optimizer (SHO) algorithm is a metaheuristic algorithm inspired by the life of spotted hyenas, introduced by Dhiman and Kumar (2017) to solve continuous optimization problems. Various studies have been performed based on changes in the SHO algorithm to solve various problems due to its effectiveness and success in solving continuous problems. This paper aims to comprehensively survey the application of the SHO algorithm in solving various optimization problems. In this paper, SHO algorithms are categorized based on hybridization, improvement, SHO variants, and optimization problems. This study invites researchers and developers of meta-heuristic algorithms to employ the SHO algorithm for solving diverse problems since it is a simple and robust algorithm for solving intricate and NP-hard problems. Based on the studies, it was concluded that the SHO algorithm had been used more in optimization problems. The purpose of optimization problems is to find optimal solutions and finding global points in the problem environment. Also, the SHO algorithm establishes a good trade-off between the exploration and extraction stages. Based on the done studies and investigations, properties and factors of the SHO algorithm are better than another meta-heuristic algorithms, which has increased its adaptability and flexibility in different fields.

Software engineering is about understanding and making the right tradeoffs. Many software engineering tasks-such as test case generation, design, sprint planning, and refactoring-boil down to understanding tradeoffs including concretely expressing the attributes to optimize and elements and decisions that will maximize their intended outcomes. Consequently, many software engineering problems can be formulated as search problems. Driven by these observations, Harman and Jones in 2001 emphasized the importance of concentrated research on the application of search-based techniques in software engineering and coined the subfield of search-based software engineering (SBSE) in software engineering research.1 During the two decades that followed, SBSE has seen a significant amount of increased research where metaheuristic algorithms are used to create recommendations for software engineering tasks.

This article proposes a new metaheuristic algorithm called Archimedes optimization algorithm (AOA) for solving optimization problems of optimal power flow (OPF) utilizing the renewable energy sources (RES) for minimizing different single-objective and multi-objective functions based on minimization of fuel cost, power losses of transmission lines, emission and voltage profile improvement. Also, mathematical formulation of (OPF) is introduced by converting the function with multiple objectives based on price and weighting parameters into a single objective function. Also, the effect of optimal RES is merged into the OPF problem. Notably, optimal RES placement yields even more effective solution. AOA was inspired by an intriguing physical law known as Archimedes' Principle. To prove the effectiveness of the AOA proposed algorithm, it compared with different recent algorithms for solving the optimal power flow problems and testing them to one standard system of the IEEE30-bus test system. The superiority of the proposed AOA algorithm is proven also by applying them on the IEEE30-bus modified system with optimal allocation of renewable energy source (RES). The results demonstrate that the proposed algorithm is more successful and efficient than the other optimization methods in the title of resolving OPF problems.

Nowadays, the world is encountering multiple challenges of energy security, economic recovery, and the effect of global warming. Investing in new fossil fuels only locks in uneconomic practices, sustains existing risks and increases the threats of climate change. In contrast, renewable energies, such as photovoltaic energy, constitute one of the most promising technologies in combating global increase in temperatures. Given its simplicity and low maintenance costs, photovoltaic energy is the most effective alternative to address the issues above. However, the standard test conditions (STCs) of PV modules are, in most cases, different from the real working conditions of a solar module. For instance, high levels of incident irradiation in an arid climate may cause the temperature of a module to rise by many degrees above the STC temperature of 25 °C, lowering the module’s performance. To effectively simulate and control PV systems for a given location, it has become paramount to develop a robust and accurate model that considers how PV modules behave. This study seeks to introduce an emerging metaheuristic optimization algorithm to estimate the unknown parameters of PV modules. The strategies deployed by flying foxes in the event of high temperatures have given birth to the development of a new metaheuristic algorithm called FFO. Contrary to previous methods, this new modeling procedure makes it possible to calculate all the parameters, regardless of temperature or irradiance. Four PV modules, having different technologies, were tested to evaluate the accuracy of the algorithm in question. The effectiveness of FFO is then contrasted with other well-known metaheuristics where single and double diode models are deployed. The results show that the FFO optimizer represents a substantial and compelling substitute for PV module extraction methods.

Ngày nay, thiết kế tối ưu công trình sử dụng thuật toán metaheuristic được sử dụng một cách thường xuyên hơn nhờ hiệu quả vượt trội của các thuật toán tối ưu này trong việc giải quyết các bài toán có độ phức tạp cao. Nhờ những ưu điểm đó, có khá nhiều thuật toán tối ưu đã và đang được đề xuất. Tuy nhiên, tùy thuộc vào đặc điểm của từng dạng bài toán tối ưu khác nhau mà hiệu quả của thuật toán sẽ khác nhau. Trong bài báo này, bài toán tối ưu của khung thép sử dụng phân tích phi tuyến đã được xem xét. Bốn thuật toán tối ưu sử dụng phổ biến gần đây cho dạng bài toán này được xem xét so sánh bao gồm: bản nâng cấp thuật toán tiến hóa vi phân (EpDE), bản nâng cấp thuật toán tối ưu va chạm giữa các vật chất (ECBO), kỹ thuật tìm kiếm hài hòa (HS) và thuật toán Rao. Một khung thép phẳng 5 tầng được tối ưu với hàm mục tiêu là tổng khối lượng và điều kiện ràng buộc cả về khả năng chịu tải và về chuyển vị lệch tầng. Kết quả tính toán cho thấy, thuật toán EpDE không chỉ tìm kiếm được kết quả tối ưu tốt nhất mà tốc độ tối ưu cũng tốt nhất trong bốn thuật toán.

Doğadaki canlıların sürü davranışlarından, bitkilerden, insana özgü olgulardan, fizik, matematik, biyoloji ve kimya gibi bilimsel alanlardaki olaylardan esinlenen onlarca metasezgisel optimizasyon yöntemi mevcuttur. Bu yöntemler belirli problemlerde başarılı olmakla birlikte bütün problemlerde başarılı olamamaktadır. Bundan dolayı araştırmacılar tarafından her geçen gün yeni metasezgisel yöntemler önerilmektedir. Bu çalışmada ilk defa güncel Yapay Deniz Anası Optimizasyonu, Etçil Bitki Optimizasyonu, Giza Piramitleri İnşaatı Optimizasyonu, Gradyan Tabanlı Optimizasyon, Öğrenci Psikolojisine Dayalı Optimizasyon ve Tunik Sürüsü Optimizasyonu olmak üzere altı güncel metasezgisel optimizasyon algoritması 10 adet matematiksel kalite testi foksiyonunda 10, 30 ve 50 boyut değerleri baz alınarak ayrıntılı bir şekilde karşılaştırılmıştır. Elde edilen sonuçlara göre 10 kalite testinden 7’sinde en iyi sonuçları Öğrenci Psikolojisine Dayalı Optimizasyon vermiştir. Gradyan Tabanlı Optimizasyon’un ise 4 kalite testinde en iyi sonuçları verdiği görülmüştür. En kötü performansı ise Etçil Bitki Optimizasyonu ve Tunik Sürüsü Optimizasyonu göstermiştir. Süre bakımından karşılaştırmak üzere algoritmalar 50 boyutlu test fonksiyonlarında 1000 iterasyonda çalıştırılmış ve elde edilen ortalama çalışma süreleri incelendiğinde, Yapay Deniz Anası Optimizasyonu ve Tunik Sürüsü Optimizasyonu’nun en hızlı çalışan algoritmalar olduğu görülmektedir. Etçil Bitki Optimizasyonu ve Öğrenci Psikolojisine Dayalı Optimizasyon ise en yavaş çalışan algoritmalar olmuştur. There are dozens of metaheuristic optimization methods inspired by the swarm behaviors of creatures in nature, plants, human-specific phenomena, events in scientific fields such as physics, mathematics, biology and chemistry. These methods are successful in certain problems but not in all problems. Therefore, new metaheuristic methods are suggested by researchers frequently. In this study, for the first time, six up-to-date metaheuristic optimization algorithms, namely Jellyfish Search Optimizer, Carnivorous Plant Algorithm, Giza Pyramids Construction Algorithm, Gradient Based Optimizer, Student Based Psychology Optimization and Tunicate Swarm Optimization, were compared by using 10 mathematical benchmark functions with 10, 30 and 50 dimensions. According to the results obtained, Student Based Psychology Optimization gave the best results in 7 out of 10 benchmark functions. It was observed that Gradient Based Optimizer gave the best results in 4 benchmark functions. Carnivorous Plant Algorithm and Tunicate Swarm Optimization showed the worst performance. In order to compare in terms of time, algorithms were run at 1000 iterations in 50- dimensional benchmark functions. When the average run times obtained are examined, it is seen that Jellyfish Search Optimizer and Tunicate Swarm Optimization are the fastest running algorithms. Carnivorous Plant Algorithm and Student Based Psychology Optimization were the slowest running algorithms.

In this paper, a novel flower pollination algorithm (FPA) is implemented to solve the problem of combined economic emission dispatch (CEED) in the power system. The FPA is a new metaheuristic optimization technique, which takes a biological approach to flower pollination. The FPA mimics the characteristics of flower pollination according to the survival of the fittest concept. CEED represents a combination of the emission and economic dispatch functions, formulated into a single function using the penalty factor. In this paper, the effect of valve point loading in the power system network is considered to obtain minimum fuel cost, minimum emissions, and optimum power generation. The performance of the proposed algorithm is evaluated using two test systems, namely 10 and 14 generating units by contemplating the valve point loading effect as well as transmission loss. The results of the 10 and 14 system units are compared with a learning-based optimization technique to demonstrate the effectiveness of the FPA. The findings reveal that the proposed FPA gives better performance than other algorithms with minimum fuel cost and emissions.

This paper reports a study of the flowshop scheduling problem with two machines where a set of tasks must be scheduled on a set of machines respecting the same fixed order. The objective was to des...

This paper presents a novel methodology for path generation synthesis of the four-bar mechanism. A new objective function for the path generation synthesis problem, namely, the Geometrical Similarity Error Function (GSEF), is introduced. Indeed, GSEF assesses the similarity between generated and desired paths, and its number of design variables is less than those in the other synthesis methods. Then, using an Innovative Adaptive Algorithm (IAA), some operators are utilized for matching two similar paths. GSEF-IAA methodology has some significant advantages over the reported synthesis methods. The method is fast, takes much less CPU time, and saves a large amount of computer memory. Four path generation problems are solved using GSEF-IAA, and the results are compared with those of previous methods using some well-known optimization algorithms to demonstrate the efficiency of GSEF-IAA methodology.

The development of fast and reliable optimization algorithms is required in order to obtain real-time optimal trajectory on-board spacecraft. In addition, the wide spread of small satellites, due to their low costs, is leading to a greater number of satellite formations in space. This paper presents an Improved version of the Magnetic Charged System Search (IMCSS) metaheuristic algorithm to compute time-suboptimal manoeuvres for satellite formation flying. The proposed algorithm exploits some strategies aimed at improving the convergence to the optimum, such as the chaotic local search and the boundary handling technique, and it is able to self-tune its internal parameters and coefficients. Moreover, the inverse dynamics technique and the differential flatness approach, through the B-splines curves, are used to approximate the trajectory. The optimization procedure is applied to the circular J2 relative model developed by Schweighart and Sedwick and to the elliptical relative motion model developed by Yamanaka and Ankersen. The results of this paper show that the convergence is better achieved by using the proposed tools, thus proving the efficiency and reliability of the algorithm in solving some space engineering problems.

Nowadays, mixed-model assembly lines (MMALs) are extensively applied to manufacture different products with no need for the changeover of the whole lines to satisfy the diversified preferences of c...

Recent years have witnessed the use of metaheuristic algorithms to solve the optimization problems that usually require extensive computations and time. Among others, scatter search is the widely used evolutionary metaheuristic algorithm. It uses the information of global optima, which is stored in a different and unique set of solutions. In this paper, an updated review of scatter search (SS) is given. SS has been successfully applied in a variety of research problems, namely, data mining, bioinformatics, and engineering design. This paper presents the modified and hybrid versions of SS with their applications. The control strategies are discussed to show their impact on the performance of SS. various issues and future directions related to SS are also discussed. It inspires innovative researchers to use this algorithm for their research domains.

Many mathematical optimization problems from real-life applications are NP-hard, and hence no algorithm that solves them to optimality within a reasonable time is known. For this reason, metaheuristic methods are mostly preferred when their size is big. Many meta-heuristic methods have been proposed to solve various combinatorial optimization problems. One of the newly introduced metaheuristic methods is a bat-inspired algorithm, which is based on the echolocation behaviour of microbats. Bat algorithm (BA) and its variants have been used successfully to solve several optimization problems. However, from the No-free Lunch Theorem, it is known that there is no universal metaheuristic method that can solve efficiently all optimization problems. Thus, this study work focused on investigating the usefulness of BA in solving an optimization problem called Course Teaching Problem (CTP). Since BA was originally designed to solve continuous problems, and CTP is a combinatorial optimization problem, a discrete version of BA for CPT has been proposed and tested using real-life data from the Dar es Salaam University College of Education (DUCE). The algorithm has produced promising results, as in each execution test, it generated a timetable in which all hard constraints were met and soft constraints were significantly satisfied within a few iterations. Keywords: Combinatorial optimization, Timetabling problem, Metaheuristic algorithms, Bat algorithm.

Purpose Optimal placement of static VAR compensator (SVC) devices not only improves the voltage profile (VP) but also reduces the active power loss (APL) and enhances the voltage stability (VS) through injecting appropriate VARs at optimal buses. The traditional mathematical methods may not provide global best solution and pose difficulties in handling multi-objective SVC placement (SVCP) problem with complex constraints and forcefully place all the given number of SVCs in the system without assessing their real requirements in enhancing the chosen performances. The purpose of this paper is to formulate the SVCP as a multi-objective optimization problem and solve it using a metaheuristic algorithm for global best solution. Design/methodology/approach The proposed SVCP method uses improved harmony search optimization (IHSO) with dissonance-avoiding mechanism for obtaining the global best solution through driving away the solution from the sub-optimal traps. In addition, the method uses a self-adaptive technique for optimally tuning the IHSO parameters and places only the required number of SVCs from the given number of SVCs. Findings This paper presents the results of the proposed method for 14, 30 and 57 bus systems and exhibits that the proposed method outperforms the existing SVCP methods in achieving the desired performances. Originality/value This paper proposes a new self-adaptive IHSO based SVCP method for optimally placing only the required number of SVCs with a goal of attaining the global best performances.

Metaheuristic algorithms are successful methods of optimization. The firefly algorithm is one of the known metaheuristic algorithms used in a variety of applications. Recently, a new and efficient version of this algorithm was introduced as NEFA, which indicated a good performance in solving optimization problems. However, the introduced attraction model in this algorithm may not provide good coverage of the search space and thus trap the algorithm in a local optimum. In this paper, a new and efficient improved firefly algorithm called INEFA is proposed to improve the performance of NEFA. In INEFA, a new model of attraction is introduced in which each firefly can be attracted to brighter fireflies located in different areas of the search space, using the clustering concept to classify fireflies. To evaluate the performance of INEFA, it was used to optimize several known benchmark functions. The results were compared with the results of the firefly algorithm and some of its known improvements. The comparison of results indicated the significant power of INEFA compared to the algorithms. It was used to evaluate its application in solving a constrained optimization problem. The comparison results showed that INEFA performs better than most of the compared algorithms.

The aim of this paper is to present a design of a metaheuristic search called improved monkey algorithm (MA+) that provides a suitable solution for the optimization problems. The proposed algorithm has been renewed by a new method using random perturbation (RP) into two control parameters (p1 and p2) to solve a wide variety of optimization problems. A novel RP is defined to improve the control parameters and is constructed off the proposed algorithm. The main advantage of the control parameters is that they more generally prevented the proposed algorithm from getting stuck in optimal solutions. Many optimization problems at the maximum allowable number of iterations can sometimes lead to an inferior local optimum. However, the search strategy in the proposed algorithm has proven to have a successful global optimal solution, convergence optimal solution, and much better performance on many optimization problems for the lowest number of iterations against the original monkey algorithm. All details in the improved monkey algorithm have been represented in this study. The performance of the proposed algorithm was first evaluated using 12 benchmark functions on different dimensions. These different dimensions can be classified into three different types: low-dimensional (30), medium-dimensional (60), and high-dimensional (90). In addition, the performance of the proposed algorithm was compared with the performance of several metaheuristic algorithms using these benchmark functions on many different types of dimensions. Experimental results show that the improved monkey algorithm is clearly superior to the original monkey algorithm, as well as to other well-known metaheuristic algorithms, in terms of obtaining the best optimal value and accelerating convergence solution.

Considering ergonomic factors in assembly line balancing problems can significantly improve human ergonomic conditions. In this study, a two-stage framework is developed to balance a mixed-model parallel U-shaped assembly line by considering ergonomic risks. In the first stage, according to various ergonomic standards and using the best-worst method and ELECTRE TRI, ergonomic risks of each task are identified and each task is classified into hard, normal, or easy class. In the second stage, a mathematical model of the problem is developed by considering a new objective function to level the number of tasks of each class between stations. This objective ensures that the tasks of each class are distributed equally among stations, resulting in having a roughly equal number of hard, normal, and easy tasks in each station. To solve the problem, a constraint programming model and a heuristic algorithm are developed. Numerical results show that the proposed objective function can level ergonomic risks well. The performance of the proposed solution approaches is examined against six different metaheuristic algorithms. Numerical results indicate the superiority of the constraint programming model for small and medium-size problems and that of the heuristic algorithm for large-size problems.

The complexity of an optimization problem is determined by its decision and objective spaces. Over the past few decades, a large number of works have focused on the performance improvement of metaheuristic algorithms via the objective space, whereas studies related to the decision space have attracted little attentions. Moreover, metaheuristic algorithms may not obtain satisfactory results within an entire feasible region, even if sufficient computational resources are available. Therefore, reducing the search space (i.e., finding a trust region) may be an effective method to ensure that the convergence is sufficiently close to the global optimal region. However, inappropriate subspace size may also weaken the performance of algorithms except for ones with a sufficiently small search space. To alleviate aforementioned problems, a variable search space (VSS) strategy based on a sequential trust region determination approach is proposed in this paper. In the VSS, the entire optimization process is divided into two stages: the first stage is to use an optimization approach for sequentially finding the trust domain of each variable and then determine the best-matched subspace; the second stage is to employ the optimization method for searching an optimal/near-optimal solution within the found trust region. The effectiveness of the VSS is evaluated using two widely used test suites, that is, IEEE CEC2014 and BBOB2012. Experimental results indicate that improving the algorithm performance is an important method for tackling problems, but locating a trust region is also beneficial for metaheuristic algorithms to improve the solution precision, especially for complex optimization problems.

Focus is limited and singular in many image capture devices. Therefore, different focused objects at different distances are obtained in a single image taken. Image fusion can be defined as the acquisition of multiple focused objects in a single image by combining important information from two or more images into a single image. In this paper, a new multi-focus image fusion method based on Bat Algorithm (BA) is presented in a Multi-Scale Transform (MST) to overcome limitations of standard MST Transform. Firstly, a specific MST (Laplacian Pyramid or Curvelet Transform) is performed on the two source images to obtain their low-pass and high-pass bands. Secondly, optimization algorithms were used to find out optimal weights for coefficients in low-pass bands to improve the accuracy of the fusion image and finally the fused multi-focus image is reconstructed by the inverse MST. The experimental results are compared with different methods using reference and non-reference evaluation metrics to evaluate the performance of image fusion methods.

Infrastructure development is followed by an increase in demand for ready-mix concrete (RMC). Some failures in delivering RMC on time would delay construction performance or the truck would need to wait at the construction site, so the delivered concrete became useless if it exceeded the concrete setting time limit. This research optimizes the supply chain of ready mix concrete based on real-time data from a batching plant in Surabaya by using the metaheuristic method. The metaheuristic method used in this research are Particle Swarm Optimization (PSO) and Symbiotic Organisms Search (SOS), and also compares the performance of both metaheuristic algorithms, because both algorithms have proven effective in solving various optimization problems. The objective function of this research is to minimize the total waiting times of mixer trucks. The results showed that the SOS algorithm has better performance than the PSO algorithm, which gets lower total waiting times of 100,863 hours.

Modeling of solar photovoltaic cell is an essential requirement in the computations involved in solar photovoltaic power systems. Some metaheuristic algorithms are used for determining the cell par...

The design of complex mechanical components is a time-consuming process which involves many design variables with multiple interacted objectives and constraints. Traditionally, the design process of mechanical components is performed manually depending on the intuition and experience of the designer. In recent decades, automatic methods have been proposed to effectively search diverse and large parameter spaces. There is a growing interest in design optimization of mechanical systems using metaheuristic algorithms to improve the product lifecycle and performance and minimize the cost. Nowadays, there is a growing interest in design optimization of mechanical systems using metaheuristic algorithms to improve the product lifecycle and performance and minimize the cost. This review article demonstrates the applications of different metaheuristic algorithms in enhancing the design process of different mechanical systems. First, the basic concepts of common used metaheuristic algorithms are introduced. Then the applications of theses algorithms in optimization of different mechanical systems are discussed.