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Abstract This paper introduces a new metaheuristic, Single Seekers Society (SSS) algorithm, for solving unconstrained and constrained continuous optimization problems. The proposed algorithm aims to simulate the behaviour of a group of people living together, both individually and holistically. The SSS algorithm brings together several single-solution based search algorithms, single seekers, while realizing an information sharing mechanism based on the superposition principle and the reproduction procedure. Each single seeker tries to improve one single solution by using randomly generated parameter set until the stopping condition is reached. Then, the SSS algorithm exchanges partial information between the best solutions identified by the single seekers via the reproduction process. This characteristic generates new solutions to set as the starting point of the single seekers for their next run and provides a satisfactory level of diversification for the SSS algorithm. Additionally, the SSS algorithm determines a target point via the superposition principle at each iteration to make the single seekers to direct their discovery towards this target point. Thus, the SSS algorithm has the feature providing to share the information produced by the single seekers through the reproduction and the superposition principle. The performance of the proposed SSS algorithm is tested on the well-known unconstrained and constrained continuous optimization problems through a set of computational studies. This paper compares SSS algorithm against 27 and 17 different search algorithms on unconstrained and constrained problems, respectively. The experimental results indicate the stability and the effectiveness of the SSS algorithm in terms of quality of produced results, achieved level of convergence and the capability of coping with trapping in local optima and stagnation problems. Highlights • A novel metaheuristic algorithm brings together several single-solution based heuristics for global optimization. • High converging capability. • Extensive computational study is presented for solving many test problems with success. [ABSTRACT FROM AUTHOR]

This paper introduces a new hub-and-center transportation network problem for a new company competing against an operating company. The new company intends to locate p hubs and assign the center nodes to the located hubs in order to form origin–destination pairs. It desires not only to maximize the total captured flow in the market but also aims to minimize the total transportation cost. Three competition rules are established between the companies which must be abided. According to the competition rules, the new company can capture the full percentage of the traffic in each origin-destination pair if its transportation cost for each route is significantly less than of the competitor. If its transportation cost for each route is not significantly less than one of the competitors, only a certain percentage of the traffic can be captured. A bi-objective optimization model is proposed for the hub location problem on hand under a competitive environment. As the problem is shown to be NP-hard, a novel meta-heuristic algorithm called multi-objective biogeography-based optimization is developed. As there is no benchmark in the literature, a popular non-dominated sorting algorithm is utilized to validate the results obtained. Moreover, to enhance the performance of the proposed Pareto-based algorithms, this paper intends to develop a binary opposition-based learning as a diversity mechanism for both algorithms. The algorithms are tuned to solve the problem, based on which their performances are compared, ranked, and analyzed statistically. Finally, the applicability of the proposed approach and the solution methodologies are demonstrated in three steps. [ABSTRACT FROM AUTHOR]

Abstract In this paper, a new mathematical model is presented, for the first time, for multi-item economic order quantity for growing items considering various operational constraints. The model aims to maximize company's total profit by determining the optimal values of the decision variables including: number of ordered items from each type and the time period needed to grow each type of items. To propose a realistic model which can be implemented in variety of real-world problems, different operational constraints are considered including: on-hand budget, warehouse capacity, and total allowable holding cost constraints. To solve the problem in small sizes a well-known exact solution methodology called Sequential Quadratic Programming is utilized. To solve the problem in medium and large sizes two novel hybrid metaheuristics called Sine Cosine Crow Search Algorithm and Water Cycle Moth–Flame Optimization algorithm are utilized due to sub-optimal results of the exact solution method. This is due to existence of enormous number of local optima and non-linearity of the proposed model. The performance of the algorithms is evaluated using different measures such as relative percentage deviation, percentage relative error, and CPU-Time. By solving various test problems in different sizes performance of the algorithms is evaluated and the best solution method for the problem is determined using statistical analyses. [ABSTRACT FROM AUTHOR]

In this paper a novel nature-inspired optimization paradigm is proposed called Moth-Flame Optimization (MFO) algorithm. The main inspiration of this optimizer is the navigation method of moths in nature called transverse orientation. Moths fly in night by maintaining a fixed angle with respect to the moon, a very effective mechanism for travelling in a straight line for long distances. However, these fancy insects are trapped in a useless/deadly spiral path around artificial lights. This paper mathematically models this behaviour to perform optimization. The MFO algorithm is compared with other well-known nature-inspired algorithms on 29 benchmark and 7 real engineering problems. The statistical results on the benchmark functions show that this algorithm is able to provide very promising and competitive results. Additionally, the results of the real problems demonstrate the merits of this algorithm in solving challenging problems with constrained and unknown search spaces. The paper also considers the application of the proposed algorithm in the field of marine propeller design to further investigate its effectiveness in practice. Note that the source codes of the MFO algorithm are publicly available at http://www.alimirjalili.com/MFO.html . [ABSTRACT FROM AUTHOR]

Nowadays, optimization techniques are required in various engineering domains in order to find optimal solutions for complex problems. As a result, there is a growing tendency among scientists to enhance existing nature-inspired algorithms using various evolutionary strategies and to develop new nature-inspired optimization methods that can properly explore the feature space. The recently designed nature-inspired metaheuristic, named the Golden Jackal Optimization​ (GJO) algorithm, was inspired by the collaborative hunting actions of the golden jackal in nature to solve various challenging problems. However, like other approaches, the GJO has the limitations of poor exploitation ability, ease to get stuck in a local optimal region, and an improper balancing of exploration and exploitation. To overcome these limitations, this paper proposes a novel contribution to GJO based on a new technique, namely the fast random opposition-based learning Golden Jackal Optimization algorithm (FROBL-GJO). The FROBL technique is mainly inspired by opposition-based learning (OBL) and random opposition-based learning (ROBL) techniques to enhance the optimization precision and convergence speed of the GJO algorithm. Furthermore, two other models, such as OBL-GJO and ROBL-GJO, are also proposed for comparison purposes. To examine the proficiency of the newly proposed FROBL-GJO algorithm, it has been examined with several well-known existing meta-heuristic algorithms while solving the CEC-2005 and CEC-2019 benchmark test functions and real-life engineering problems. The experimental outcomes and statistical tests reveal the superior performance of the proposed FROBL-GJO in solving both global optimization and engineering design problems. Hence, the findings of benchmark functions and engineering problems endorse that the proposed FROBL-GJO algorithm can be considered a promising method for solving complex optimization problems. • A novel strategy namely fast random opposition-based learning (FROBL) is proposed. • The FROBL is inspired by OBL and ROBL techniques to explore the search space efficiently. • FROBL is appended to the GJO algorithm to propose the FROBL-GJO algorithm. • FROBL-GJO is employed to solve different benchmark functions, and engineering problems. • FROBL-GJO generates good results compared to OBL-GJO, ROBL-GJO and other algorithms. [ABSTRACT FROM AUTHOR]

Searching for the optimal subset of features is known as a challenging problem in feature selection process. To deal with the difficulties involved in this problem, a robust and reliable optimization algorithm is required. In this paper, Grasshopper Optimization Algorithm (GOA) is employed as a search strategy to design a wrapper-based feature selection method. The GOA is a recent population-based metaheuristic that mimics the swarming behaviors of grasshoppers. In this work, an efficient optimizer based on the simultaneous use of the GOA, selection operators, and Evolutionary Population Dynamics (EPD) is proposed in the form of four different strategies to mitigate the immature convergence and stagnation drawbacks of the conventional GOA. In the first two approaches, one of the top three agents and a randomly generated one are selected to reposition a solution from the worst half of the population. In the third and fourth approaches, to give a chance to the low fitness solutions in reforming the population, Roulette Wheel Selection (RWS) and Tournament Selection (TS) are utilized to select the guiding agent from the first half. The proposed GOA_EPD approaches are employed to tackle various feature selection tasks. The proposed approaches are benchmarked on 22 UCI datasets. The comprehensive results and various comparisons reveal that the EPD has a remarkable impact on the efficacy of the GOA and using the selection mechanism enhanced the capability of the proposed approach to outperform other optimizers and find the best solutions with improved convergence trends. Furthermore, the comparative experiments demonstrate the superiority of the proposed approaches when compared to other similar methods in the literature. [ABSTRACT FROM AUTHOR]

Human Learning Optimization (HLO) is an emerging meta-heuristic with promising potential, which is inspired by human learning mechanisms. Although binary algorithms like HLO can be directly applied to mixed-variable problems that contains both continuous values and discrete or Boolean values, the search efficiency and the performance of those algorithms may be significantly spoiled due to “the curse of dimensionality” caused by the binary coding strategy especially when the continuous parameters of problems require high accuracy. Therefore, this paper extends HLO and proposes a novel hybrid-coded HLO (HcHLO) framework to tackle mix-coded problems more efficiently and effectively, in which real-coded parameters are optimized by a new continuous HLO (CHLO) based on the linear learning mechanism of humans and the other variables are handled by the binary learning operators of HLO. Finally, HcHLO is adopted to solve 14 benchmark problems and its performance is compared with that of recent meta-heuristic algorithms. The experimental results show that the proposed HcHLO achieves the best-known overall performance so far on the test problems, which demonstrates the validity and superiority of HcHLO. [ABSTRACT FROM AUTHOR]

This paper proposes an improved Arithmetic Optimization Algorithm (AOA) to train artificial neural networks (ANNs) under dynamic environments. Despite many successful applications of metaheuristic training of ANNs, these studies assume static environments, which might not be realistic in real-world nonstationary processes. In this study, the training of ANNs is modeled as a dynamic optimization problem, and the proposed AOA is used to optimize connection weights and biases of the ANN under the presence of concept drift. The proposed method is designed to work for classification tasks. The performance of the proposed algorithm has been tested on twelve dynamic classification problems. Comparative analysis with state-of-the-art metaheuristic optimization algorithms has been provided. The superiority of the compared algorithms has been verified using nonparametric statistical tests. The results show that the improved AOA outperforms compared algorithms in training ANNs under dynamic environments. The findings demonstrate the potential of improved AOA for dynamic data-driven applications. • Artificial neural networks (ANNs) are trained in dynamic environments. • An improved arithmetic optimization algorithm (AOA) is developed. • The improved AOA is used to train ANNs under concept drift. • The effectiveness of the proposed algorithm is statistically verified. [ABSTRACT FROM AUTHOR]

In this paper, a new metaheuristic algorithm called the Coati Optimization Algorithm (COA) is introduced, which mimics coati behavior in nature. The fundamental idea of COA is the simulation of the two natural behaviors of coatis: (i) their behavior when attacking and hunting iguanas and (ii) their escape from predators. The implementation steps of COA are described and mathematically modeled in two phases of exploration and exploitation. COA performance is evaluated on fifty-one objective functions, including twenty-nine functions from the IEEE CEC-2017 test suite and twenty-two real-world applications from the IEEE CEC-2011 test suite. COA's results are compared to those of eleven well-known metaheuristic algorithms. The simulation results indicate that COA has an evident superiority over the compared algorithms by balancing exploration in global search and exploitation in local search, and is far more competitive. To assess the COA's effectiveness in real-world applications, the proposed approach is implemented on the IEEE CEC-2011 test functions and four practical optimization problems, which the simulation results indicate the high capability of COA in dealing with these types of optimization problems. • A new optimization algorithm called Coati Optimization Algorithm (COA) is designed to model the natural behaviors of coatis. • COA's fundamental inspirations include attacking and hunting coatis and the escape behavior of coatis when confronted by predators. • The various stages of COA are described, then mathematically modeled in two phases of exploration and exploitation. • Sixty-eight standard benchmark functions have been employed to evaluate COA performance in solving optimization problems. • The performance of the COA in presenting the optimization results is compared with eight well-known metaheuristic algorithms. [ABSTRACT FROM AUTHOR]

This paper proposes a novel population-based optimization algorithm called Sine Cosine Algorithm (SCA) for solving optimization problems. The SCA creates multiple initial random candidate solutions and requires them to fluctuate outwards or towards the best solution using a mathematical model based on sine and cosine functions. Several random and adaptive variables also are integrated to this algorithm to emphasize exploration and exploitation of the search space in different milestones of optimization. The performance of SCA is benchmarked in three test phases. Firstly, a set of well-known test cases including unimodal, multi-modal, and composite functions are employed to test exploration, exploitation, local optima avoidance, and convergence of SCA. Secondly, several performance metrics (search history, trajectory, average fitness of solutions, and the best solution during optimization) are used to qualitatively observe and confirm the performance of SCA on shifted two-dimensional test functions. Finally, the cross-section of an aircraft's wing is optimized by SCA as a real challenging case study to verify and demonstrate the performance of this algorithm in practice. The results of test functions and performance metrics prove that the algorithm proposed is able to explore different regions of a search space, avoid local optima, converge towards the global optimum, and exploit promising regions of a search space during optimization effectively. The SCA algorithm obtains a smooth shape for the airfoil with a very low drag, which demonstrates that this algorithm can highly be effective in solving real problems with constrained and unknown search spaces. Note that the source codes of the SCA algorithm are publicly available at http://www.alimirjalili.com/SCA.html . [ABSTRACT FROM AUTHOR]

This paper develops a new metaheuristic optimization algorithm named K-means Optimizer (KO) to solve a wide range of optimization problems from numerical functions to real-design challenges. First, the centroid vectors of clustering regions are established at each iteration using K-means algorithm, then KO proposes two movement strategies to create a balance between the ability of exploitation and exploration. The decision on the movement strategy for exploration or exploitation at each iteration depends on a parameter that will be designed to recognize if each search agent is too long in the region visited with no self-improvement. To demonstrate the effectiveness and reliability of KO, twenty-three classical benchmark functions, CEC2005 and CEC2014 benchmark functions, are employed as a first example and compared with other algorithms. Then, three well-known engineering problems are also considered and their results are compared to the results obtained by the other algorithms. Finally, KO is applied to structural damage identification (SDI) problem of a complex 3D concrete structure including seven stories building having a 25.2 m total height. For this purpose, SAP2000 is used to solve the finite element (FE) model of this structure. Then, for the first time, we successfully developed a sub-program that allows two-way data exchange between SAP2000 and MATLAB through the Open Application Programming Interface (OAPI) library to update the FE model. From the results, we found that KO has the best performance for the considered benchmark functions based on the Wilcoxon rank-sum test and Friedman ranking test. The results obtained in this work have proved the effectiveness and reliability of KO in solving optimization problems, especially for SDI. Source codes of KO is publicly available at http://goldensolutionrs.com/codes.html. [ABSTRACT FROM AUTHOR]

The home energy management system (HEMS) based on advanced internet of things (IoT) technology has attracted the special attention of engineers in the field of smart grid (SG), which has the task of the demand side management (DSM) and helps to control the equality between demand and electricity supply. The main performance of HEMS is based on the optimal scheduling of home appliances because it manages power consumption by automatically controlling the loads and transferring them from peak hours to off-peak hours. This paper presents a multi-objective version of a newly introduced metaheuristic, called Arithmetic Optimization Algorithm (AOA) to discover optimal scheduling of the home appliances, which is called Multi-Objective Arithmetic Optimization Algorithm (MOAOA). Furthermore, the HEMS architecture has been programmed based on the Raspberry Pi minicomputer with Node-RED and NodeMCU modules. HEMS uses the MOAOA algorithm to find the optimal schedule pattern to reduce daily electricity costs, reduce the peak to average ratio (PAR), and increase user comfort (UC). Real-time pricing (RTP) and critical peak pricing (CPP) signals are presumed as energy tariffs. Simulations are performed in two different scenarios: (I) appliance scheduling scheme and (II) appliance scheduling scheme with the integration of renewable energy sources (RES). The results of MOAOA are compared with Multi-Objective Particle Swarm Optimization (MOPSO), Multi-Objective Gray Wolf Optimizer (MOGWO), and Multi-Objective Antlion optimization (MOALO) algorithms. The results demonstrate that the use of the presented scheme remarkably reduces the cost of electricity consumption as well as PAR, in addition to the integration of MOAOA with RES, which greatly increases user comfort. [ABSTRACT FROM AUTHOR]

As the number of dimensions of an optimization problem increases, the process of deriving the solution becomes more complicated and difficult. Metaheuristic algorithms are effective methods for solving many optimization problems, whereas most of these algorithms perform poorly on high-dimensional problems. In this paper, a novel metaheuristic algorithm called Dynamic Stochastic Search (DSS) is proposed for high-dimensional optimization problems. To effectively execute the exploration (diversification) and exploitation (intensification) processes of DSS, a dynamic stochastic search process is designed by defining a stochastic search control factor, a search process based on the Gaussian distribution, two shrink modes inspired by the Whale Optimization Algorithm, and a balance mechanism derived from the Bat Algorithm. The proposed algorithm has the following advantages: no specific control parameters other than the population size and the maximum number of iterations, a simple structure, and less computational effort in the implementation. Analyzing the computational complexity of DSS demonstrates its simplicity. To evaluate the performance of DSS, twenty 300-dimensional and 3000-dimensional classical benchmark functions as well as thirty 100-dimensional CEC2014 benchmark functions are utilized. The statistical results prove the effectiveness and feasibility of DSS for high-dimensional optimization problems because it shows better convergence performance and higher efficiency than various advanced optimization algorithms. Moreover, the superiority of DSS in solving high-dimensional real-world optimization problems is validated by applying it to feature selection for ten real-world benchmark classification datasets from the UCI machine learning repository. • A novel metaheuristic algorithm called Dynamic Stochastic Search (DSS) is proposed. • DSS is designed to solve high-dimensional optimization problems. • DSS has a simple structure, low computational complexity, and less control parameters. • Prove the effectiveness and feasibility of DSS using classical and CEC2014 benchmark functions. • Prove the superiority of DSS on high-dimensional real-world optimization problems. [ABSTRACT FROM AUTHOR]

This paper presents a novel meta-heuristic algorithm so-called White Shark Optimizer (WSO) to solve optimization problems over a continuous search space. The core ideas and underpinnings of WSO are inspired by the behaviors of great white sharks, including their exceptional senses of hearing and smell while navigating and foraging. These aspects of behavior are mathematically modeled to accommodate a sufficiently adequate balance between exploration and exploitation of WSO and to assist search agents to explore and exploit each potential area of the search space in order to achieve optimization. The search agents of WSO randomly update their position in connection with best-so-far solutions, to eventually arrive at the optimal outcome. The performance of WSO was comprehensively benchmarked on a set of 29 test functions from the CEC-2017 test suite for several dimensions. WSO was further applied to solve the benchmark problems of the CEC-2011 evolutionary algorithm competition to prove its reliability and applicability to real-world problems. A thorough analysis of computational and convergence results was presented to shed light on the efficacy and stability levels of WSO. The performance score of WSO in terms of several statistical methods was compared with 9 well-established meta-heuristics based on the solutions generated. Friedman's and Holm's tests of the results showed that WSO revealed reasonable solutions, in terms of global optimality, avoidance of local minima and solution quality, compared to other existing meta-heuristics. [ABSTRACT FROM AUTHOR]

Feature selection is an important data processing method to reduce dimension of the raw datasets while preserving the information as much as possible. In this paper, an enhanced version of Black Widow Optimization Algorithm called SDABWO is proposed to solve the feature selection problem. The Black Widow Optimization Algorithm (BWO) is a new population-based meta-heuristic algorithm inspired by the evolution process of spider population. Three main improvements were included into the BWO to overcome the shortcoming of low accuracy, slow convergence speed and being easy to fall into local optima. Firstly, a novel strategy for selecting spouses by calculating the weight of female spiders and the distance between spiders is proposed. By applying the strategy to the original algorithm, it has faster convergence speed and higher accuracy. The second improvement includes the use of mutation operator of differential evolution at mutation phase of BWO which helps the algorithm escape from the local optima. And then, three key parameters are set to adjust adaptively with the increase of iteration times. To confirm and validate the performance of the improved BWO, other 10 algorithms are used to compared with the SDABWO on 25 benchmark functions. The results show that the proposed algorithm enhances the exploitation ability, improves the convergence speed and is more stable when solving optimization problems. Furthermore, the proposed SDABWO algorithm is employed for feature selection. Twelve standard datasets from UCI repository prove that SDABWO-based method has stronger search ability in the search space of feature selection than the other five popular feature selection methods. These results confirm the capability of the proposed method simultaneously improve the classification accuracy while reducing the dimensions of the original datasets. Therefore, SDABWO-based method was found to be one of the most promising for feature selection problem over other approaches that are currently used in the literature. [ABSTRACT FROM AUTHOR]

Chimp Optimization Algorithm (ChoA) is a recently developed meta-heuristic approach which is inspired by the individual intelligence and sexual motivation of chimps. It is designed for trapping the local optima to alleviate the slow convergence speed. In this paper, a hybrid algorithm is developed which is based on the sine–cosine functions and attacking strategy of Spotted Hyena Optimizer (SHO). This hybrid algorithm is termed as Sine–cosine and Spotted Hyena-based Chimp Optimization Algorithm (SSC). This algorithm is used to find the best optimal solutions of real-life complex problems. The sine–cosine and attacking strategy of SHO algorithm is responsible for better exploration and exploitation. These strategies are applied to update the equations of chimps during the searching process to overcome the drawbacks of the ChoA algorithm such as slow convergence and local minima. Experimental results based on IEEE CEC'17 and six real-life engineering problems such as welded beam design, tension/compression spring design, pressure vessel design, multiple disk clutch brake design, gear train design, and car side crashworthiness, demonstrate the robustness, effectiveness, efficiency, and convergence analysis of the proposed SSC algorithm in comparison with other competitor approaches. Note that the source codes are available at http://www.dhimangaurav.com. [ABSTRACT FROM AUTHOR]

To handle the multiobjective optimization problems of truss-bar design, this paper introduces a new metaheuristic multiobjective optimization algorithm. The proposed algorithm is based on a recently reported single objective version of the gradient-based optimizer (GBO) inspired by the gradients of Newton's equations. The proposed algorithm is called as multiobjective gradient-based optimizer (MOGBO), in which two operators, such as local escaping operator and gradient search rule, and few vector sets are utilized in the search phase and the elitist non-dominated sorting mechanism is used for agent sorting to find Pareto optimal solutions. The proposed MOGBO is a posteriori method, and the traditional crowding distance mechanism is employed to confirm the coverage of the best solutions for the objectives of the given problem. The performance of the proposed MOGBO algorithm is verified and validated on different test cases, including 15 unconstraint benchmark test suites and eight constraint multiobjective truss bar design problems. To prove the superiority of the MOGBO algorithm, the performance is compared with state-of-the-art algorithms, such as multiobjective ant lion optimization (MOALO), multiobjective water cycle algorithm (MOWCA), multiobjective colliding bodies optimization (MOCBO), and non-dominated sorting gray wolf optimizer (NSGWO) in terms of metrics, such as hyper-volume, coverage, inverted generational distance, pure diversity, Spacing, Spread, coverage Pareto front, diversity maintenance, generational distance, and runtime. The solutions obtained by the proposed MOGBO algorithm is highly accurate and requires less runtime than the other selected algorithms. The obtained results also show the efficiency of the MOGBO in solving most of all the complex multiobjective problems. This research will be further backed up with external guidance for the future research at https://premkumarmanoharan.wixsite.com/mysite. [ABSTRACT FROM AUTHOR]

This paper proposes a new meta-heuristic algorithm inspired by horses' herding behavior for high-dimensional optimization problems. This method, called the Horse herd Optimization Algorithm (HOA), imitates the social performances of horses at different ages using six important features: grazing, hierarchy, sociability, imitation, defense mechanism and roam. The HOA algorithm is created based on these behaviors, which has not existed in the history of studies so far. A sensitivity analysis is also performed to obtain the best values of coefficients used in the algorithm. HOA has a very good performance in solving complex problems in high dimensions, due to the large number of control parameters based on the behavior of horses at different ages. The proposed algorithm is compared with popular nature-inspired optimization algorithms, including grasshopper optimization algorithm (GOA), sine cosine algorithm (SCA), multi-verse optimizer (MVO), moth–flame optimizer (MFO), dragonfly algorithm (DA), and grey​ wolf optimizer (GWO). Solving several high-dimensional benchmark functions (up to 10,000 dimensions) shows that the proposed algorithm is highly efficient for high-dimensional global optimization problems. The HOA algorithm also outperforms the mentioned popular optimization algorithms for the case of accuracy and efficiency with lowest computational cost and complexity. [ABSTRACT FROM AUTHOR]

Ontology alignment is a fundamental task to support information sharing and reuse in heterogeneous information systems. Optimizing the combination of matchers by evolutionary algorithms to align ontology is an effective method. However, such methods have two significant shortcomings: weights need to be set manually to combine matchers, and a reference alignment is required during the optimization process. In this paper, a meta-matching approach GSOOM for automatically configuring weights and threshold using grasshopper optimization algorithm (GOA) has been proposed. In this approach, the ontology alignment problem is modeled as optimizing individual fitness of GOA. A fitness function is proposed, which includes two goals: maximizing the number of matching and the similarity score. Since it does not require an expert to provide a reference alignment, it is more suitable for real-world scenarios. To demonstrate the advantages of the approach, we conduct exhaustive experiments tasks on several standard datasets and compare its performance to other state-of-the-art methods. The experimental results illustrate that our approach is more efficiently and is significantly superior to other metaheuristic-based methods. [ABSTRACT FROM AUTHOR]

This paper presents a novel, optimization algorithm called Equilibrium Optimizer (EO), inspired by control volume mass balance models used to estimate both dynamic and equilibrium states. In EO, each particle (solution) with its concentration (position) acts as a search agent. The search agents randomly update their concentration with respect to best-so-far solutions, namely equilibrium candidates, to finally reach to the equilibrium state (optimal result). A well-defined "generation rate" term is proved to invigorate EO's ability in exploration, exploitation, and local minima avoidance. The proposed algorithm is benchmarked with 58 unimodal, multimodal, and composition functions and three engineering application problems. Results of EO are compared to three categories of existing optimization methods, including: (i) the most well-known meta-heuristics, including Genetic Algorithm (GA), Particle Swarm Optimization (PSO); (ii) recently developed algorithms, including Grey Wolf Optimizer (GWO), Gravitational Search Algorithm (GSA), and Salp Swarm Algorithm (SSA); and (iii) high performance optimizers, including CMA-ES, SHADE, and LSHADE-SPACMA. Using average rank of Friedman test, for all 58 mathematical functions EO is able to outperform PSO, GWO, GA, GSA, SSA, and CMA-ES by 60%, 69%, 94%, 96%, 77%, and 64%, respectively, while it is outperformed by SHADE and LSHADE-SPACMA by 24% and 27%, respectively. The Bonferroni–Dunnand Holm's tests for all functions showed that EO is significantly a better algorithm than PSO, GWO, GA, GSA, SSA and CMA-ES while its performance is statistically similar to SHADE and LSHADE-SPACMA. The source code of EO is publicly availabe at https://github.com/afshinfaramarzi/Equilibrium-Optimizer , http://built-envi.com/portfolio/equilibrium-optimizer/ and http://www.alimirjalili.com/SourceCodes/EOcode.zip. • Developed a novel optimization algorithm inspired by mass balance models. • Tested EO against well-studied mathematical and engineering benchmarks. • Compared the algorithm to other well-known meta-heuristics. • Demonstrated effectiveness and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, an efficient surrogate-assisted particle swarm optimization algorithm is proposed to further improve the efficiency for optimization of high-dimensional expensive problems, which sometimes involve costly simulation analysis. Unlike several surrogate-assisted metaheuristic algorithms, the proposed algorithm can effectively balance the prediction ability of surrogates and the global search ability of particle swarm optimization in the optimization process. Specifically, the proposed algorithm efficiently uses the optima obtained from the global surrogate built in the entire design space and the local surrogate built in a neighbor region around the personal historical best particle to update the velocities of the particles. It should be stressed that the neighbor region partition strategy used to obtain the optimums of local surrogates is an essential aspect of the proposed algorithm. This strategy helps to obtain the predicted optima of local surrogates in the neighbor regions to guide the search of particle swarm optimization in the optimization process. In addition, the neighbor region partition strategy considers the diversity of personal historical best particles, which enables the proposed algorithm to efficiently search for different types of problems. Moreover, the optimization efficiency of the proposed algorithm can be enhanced by using the surrogate prescreening strategy. In order to validate the proposed algorithm, it is tested on several high-dimensional numerical benchmark problems and comprehensively compared with several optimization algorithms. The results show that the proposed algorithm is very promising for the optimization of high-dimensional expensive problems. [ABSTRACT FROM AUTHOR]