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Accurate structural damage identification calls for dense sensor networks, which are becoming more feasible as the price of electronic sensing systems reduces. To transmit and process data from all nodes of a dense network is a computationally expensive BIG DATA problem; therefore scalable algorithms are needed so that inferences about the current state of the structure can be made efficiently. In this paper, an iterative spatial compressive sensing scheme for damage existence identification and localization is proposed and investigated. At each iteration, damage existence is identified from randomly collected sparse samples and damage localization is iteratively detected via sensing-processing cycles with metaheuristic sampling distribution updating. Specifically, simulated annealing and ant colony analogy are used for guidance in future selection of sensing locations. This framework is subsequently validated by numerical and experimental implementations for gusset plate crack identification. Copyright © 2016 John Wiley & Sons, Ltd. [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]

Meta-heuristic methods are global optimization algorithms which are widely used in the engineering issues, nowadays. The main problem with the classical optimization algorithms is their slow rate of convergence to time-consuming mathematical calculations. In this paper, a new stochastic search for optimization is presented using variable variance Guassian distribution sampling. The main idea of searching in this algorithm is to regenerate new samples around each solution with a Guassian distribution. The proposed algorithm is applied to four popular test functions for optimizations (Griewank, Booth, Rosenbrock, Rastrigin). Numerical simulations have revealed that the new presented algorithm outperformed simulated annealing and genetic algorithms. [ABSTRACT FROM AUTHOR]

Real parameter optimization is an important task in almost all engineering applications. This paper introduces a novel multiagent architecture and agent interaction mechanism for the solution of single objective type real-parameter optimization problems. The proposed multiagent system includes several metaheuristics as problem-solving agents that act on a common population containing the frontiers of search process and a common archive keeping the promising solutions extracted so far. Each session of the proposed architecture includes two phases: a tournament among all agents to determine the currently best performing agent and a search procedure conducted by the winner. In the tournament phase, each agent performs a fixed number of fitness evaluations over the common population and gets a success score in terms the fitness improvements it achieved by itself. The agent with the best score is the winner of the tournament. Then, the winner agent is allowed to conduct its search algorithm using the common population until its procedure gets stuck at a locally optimal solution or maximum fitness evaluations per session is reached. Afterwards, the procedure restarts with another tournament to determine the next winner. In all phases and iterations of the proposed framework, all agents use the same population and archive in conducting their search procedures. This way, agents compete with each other in terms of their fitness improvements achieved over a fixed number of fitness evaluations in tournaments, and they cooperate by sharing their search experiences through accumulating them in a common population and a common archive. The proposed multiagent system is experimentally evaluated using the well-known CEC2005 benchmark problems set. Analysis of the obtained results exhibited that the proposed framework performs significantly better than its state-of-the-art competitors in almost all problem instances. [ABSTRACT FROM AUTHOR]

Population growth and socioeconomic changes in developing countries over the past few decades have created severe stresses on the available water resources across the world, particularly in arid and semiarid regions, which are predominant in Iran. Hence, the optimal management of water resources is imperative. Reservoir operation is a challenging problem that involves complexities in terms of nonlinear functions, larger numbers of decision variables, and multiple constraints. Evolutionary or metaheuristic algorithms have become an attractive alternative to the classical methods for solving complex reservoir problems. This paper applies a metaheuristic algorithm named the firefly algorithm (FA) to reservoir operation and demonstrates the superiority of this algorithm against the genetic algorithm (GA), a commonly used optimization algorithm, using (1) five mathematical test functions, (2) the operation of a reservoir system with the purpose of irrigation supply, and (3) the operation of a reservoir system with the purpose of hydropower production. The results demonstrate the superior performance of the FA in terms of the convergence rate to global optima and of the variance of the results about global optima when compared with the results of the GA. [ABSTRACT FROM AUTHOR]

Meta-heuristic techniques are widely acknowledged as effectual methods for many optimization problems. This primary objective of this paper introduces the fundamentals of Meta-heuristic optimization algorithms. It briefs the contents and conceptions that are utilized in different Meta-heuristics to investigate their similarities and differences. The classification adopted in this discipline differentiates between proper single results based Meta-heuristics and population-based Meta-heuristics. The literature survey is concurred by the manifestation of references, application and recent trends in this field. [ABSTRACT FROM AUTHOR]

• Ranked-based mechanism-assisted Biogeography optimization (RBBO) is proposed • RBBO uses a successful adaptive random differential mutation (SarDM) mechanism • RBBO is adopted to solve 23 benchmark optimization functions and reservoir system • The proposed RBBO is verified with several well-established optimization algorithms • RBBO results indicated the superiority in solving complex optimization problems The resolution of an optimization issue is a topic studied and debated by academics from a wide variety of areas continuously. Without an optimal solution, a lot of time and resources are likely to be wasted, and the issue will remain unsolved in the worst-case scenario. Due to these concerns, researchers are increasingly resorting to the development of robust meta-heuristic optimization techniques to improve current methods while creating new population-based approaches capable of exploring the required feature space. Therefore, biogeography-based optimization (BBO) is proposed in this paper, which is impacted by species' movement and emigration across islands in quest of better favorable environments. The BBO is indeed a population-based optimization approach used to solve complicated optimization issues. However, due to the nature of its operators, this technique might become stuck in sub-optimal solutions, which slows down convergence and increases the amount of computing time needed to find optimal solutions. In order to address these issues, this research proposes a BBO version that incorporates a ranked-based strategy (RBS), an exponential dynamic Brownian random differential (EBD) mechanism, and a successful adaptive random differential mutation (SarDM) mechanism to find a more effective solution in the feasible region. RBBO is the name given to the suggested approach. It has been evaluated for addressing four standard engineering problems with restrictions and 23 benchmark functions (seven unimodal, eight multimodal, and ten composite functions). The experimental findings and evaluations show that the suggested technique outperforms the conventional BBO's efficiency and accuracy. In the case of engineering problems, for instance, the four-reservoir problem, the proposed RBBO can provide superior results in terms of the average objective function (308.71) and standard deviation (0.16) over 30 different runs compared with the other optimization methods. We expect the community to use the suggested BBO-based technique to solve more challenging tasks. [ABSTRACT FROM AUTHOR]

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

An intrusion detection system (IDS) is considered critical for detecting threats, intrusions, and unauthorized access. IDS monitors massive network traffic that includes irrelevant and extravagant features that profoundly impact the system's efficiency and slow down the classification process for accurate decisions. Its effectiveness is tested over the various techniques that comprise an enormous volume of data and heavy network traffic. Many approaches, such as machine learning algorithms, data mining, swarm intelligence, and artificial neural networks (ANN), have been implemented for adequate and improved IDSs. This paper recommends a novel feature selection method using a genetic algorithm (GA) that determines the optimal feature subsets from the NSL-KDD dataset. Further, hybrid classification has been performed using logistic regression (LR) and decision tree (DT) to achieve a better detection rate (DR) and accuracy (ACC). This research applied and compared several meta-heuristic algorithms' performance to optimize the selected optimal features. The experimental results show that the grey wolf optimization (GWO) algorithm gives the best accuracy of 99.44% and DR of 99.36% with the reduction of features (=20) out of (=41). The results of the proposed work are compared with the existing feature selection methods to verify improved performance. [Display omitted] • Novel feature selection method using a genetic algorithm (GA) that determines the optimal feature subsets. • Hybrid classification to achieve a better detection rate (DR) and accuracy (ACC). • Compared several meta-heuristic algorithms' performance to optimize the selected optimal features. • The grey wolf optimization (GWO) algorithm gives the best accuracy of 99.44% and DR of 99.36% with the reduction of features (=20) out of (=41). • Optimization for improvement of IDS. [ABSTRACT FROM AUTHOR]

This paper proposes a novel nature-inspired meta-heuristic optimization algorithm, called Whale Optimization Algorithm (WOA), which mimics the social behavior of humpback whales. The algorithm is inspired by the bubble-net hunting strategy. WOA is tested with 29 mathematical optimization problems and 6 structural design problems. Optimization results prove that the WOA algorithm is very competitive compared to the state-of-art meta-heuristic algorithms as well as conventional methods. The source codes of the WOA algorithm are publicly available at http://www.alimirjalili.com/WOA.html [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]

In these recent years, several metaheuristic optimization algorithms have been developed to solve complex real-world problems. However, according to the no free lunch theorem, there is no metaheuristic algorithm which has the best suited for solving all optimization problems. Therefore, developing new metaheuristic algorithms is an open problem. Two major categories of metaheuristics are swarm intelligence and evolutionary algorithms. Both of them have their own advantages. In this paper, we present a naturally swarm and evolutionary algorithm in which its particles have memory, collaboration, information sharing, and competition inherited from one specific analogy. So, it has the advantages of both swarm and evolutionary algorithms without complications of hybridizing them. We formulate a new metaheuristic optimization algorithm called open source development model algorithm (ODMA) inspired by open source development model and communities, in such a way that each potential solution is considered as a software, and by evolution of the softwares, better solutions of the function that should be optimized are searched. The main operations of the algorithm include employing features and methods of leading softwares, evolution of leading softwares based on their history and forking from leading softwares. After a detailed formulation and explanation of its implementation, the algorithm is evaluated by $$17$$ well-known benchmark functions, and the results are compared with PSO, ICA, and GA. The results show that ODMA outperforms GA, PSO, and ICA in terms of finding the global optimum and convergence. [ABSTRACT FROM AUTHOR]

In the past three decades nature-inspired and meta-heuristic algorithms have dominated the literature in the broad areas of search and optimization. Harmony search algorithm (HSA) is a music-inspired population-based meta-heuristic search and optimization algorithm. The concept behind the algorithm is to find a perfect state of harmony determined by aesthetic estimation. This paper starts with an overview of the harmonic phenomenon in music and music improvisation used by musicians and how it is applied to the optimization problem. The concept of harmony memory and its mathematical implementation are introduced. A review of HSA and its variants is presented. Guidelines from the literature on the choice of parameters used in HSA for effective solution of optimization problems are summarized. [ABSTRACT FROM AUTHOR]

Teaching-learning-based optimization (TLBO) is a rising star technique among metaheuristic techniques with highly competitive performance. This technique, which has been recently introduced, is based on the effect of influence of a teacher on learners and learners on their colleagues. This paper intends to apply an improved version of TLBO in the field of electromagnetics. To demonstrate its effectiveness in this area, the proposed technique is applied to two benchmarks related to brushless direct current wheel motor problem and testing electromagnetic analysis methods problem number 22. The quality of the results presented shows that the proposed technique is very competitive with other well-known optimization techniques; hence, it is a promising alternative technique for optimization in the field of electromagnetics. [ABSTRACT FROM AUTHOR]

Evolutionary population dynamics (EPD) deal with the removal of poor individuals in nature. It has been proven that this operator is able to improve the median fitness of the whole population, a very effective and cheap method for improving the performance of meta-heuristics. This paper proposes the use of EPD in the grey wolf optimizer (GWO). In fact, EPD removes the poor search agents of GWO and repositions them around alpha, beta, or delta wolves to enhance exploitation. The GWO is also required to randomly reinitialize its worst search agents around the search space by EPD to promote exploration. The proposed GWO-EPD algorithm is benchmarked on six unimodal and seven multi-modal test functions. The results are compared to the original GWO algorithm for verification. It is demonstrated that the proposed operator is able to significantly improve the performance of the GWO algorithm in terms of exploration, local optima avoidance, exploitation, local search, and convergence rate. [ABSTRACT FROM AUTHOR]

In this paper, a novel method is presented to obtain stable reduced-order model of higher order linear time invariant continuous systems using optimization technique. In order to perform optimization, a new meta-heuristic search algorithm, called Cuckoo Search (CS) is applied which is inspired by the natural behaviour of cuckoo species including the Lévy flight behaviour of some birds and fruit flies. The effectiveness of the proposed method is supported by some numerical examples of single input single output (SISO) systems and the results are compared with other well-known methods available in the literature in terms of most popular performance indices like integral square error, integral absolute error, and integral time absolute error. The method is extended for multi input multi output (MIMO) systems also. [ABSTRACT FROM PUBLISHER]

This paper presents a comparative study on different surrogate models for an application to multidimensional optimal design of electromagnetic devices. The surrogate model is combined with a heuristic optimization algorithm, which usually requires a lot of objective function calls. For this, various Kriging surrogate models, including dynamic Kriging (DK) and multiquadric radial basis function with optimal shape parameter, are investigated and discussed. In the DK surrogate model, the optimal combination of the basis functions is obtained using an improved binary particle swarm optimization algorithm. [ABSTRACT FROM PUBLISHER]

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]

This paper presents a novel and effective technique for extracting multiple ellipses from an image. The approach employs an evolutionary algorithm to mimic the way animals behave collectively assuming the overall detection process as a multi-modal optimization problem. In the algorithm, searcher agents emulate a group of animals that interact with each other using simple biological rules which are modeled as evolutionary operators. In turn, such operators are applied to each agent considering that the complete group has a memory to store optimal solutions (ellipses) seen so far by applying a competition principle. The detector uses a combination of five edge points as parameters to determine ellipse candidates (possible solutions), while a matching function determines if such ellipse candidates are actually present in the image. Guided by the values of such matching functions, the set of encoded candidate ellipses are evolved through the evolutionary algorithm so that the best candidates can be fitted into the actual ellipses within the image. Just after the optimization process ends, an analysis over the embedded memory is executed in order to find the best obtained solution (the best ellipse) and significant local minima (remaining ellipses). Experimental results over several complex synthetic and natural images have validated the efficiency of the proposed technique regarding accuracy, speed, and robustness. [ABSTRACT FROM AUTHOR]

The performance of any algorithm will largely depend on the setting of its algorithm-dependent parameters. The optimal setting should allow the algorithm to achieve the best performance for solving a range of optimization problems. However, such parameter tuning itself is a tough optimization problem. In this paper, we present a framework for self-tuning algorithms so that an algorithm to be tuned can be used to tune the algorithm itself. Using the firefly algorithm as an example, we show that this framework works well. It is also found that different parameters may have different sensitivities and thus require different degrees of tuning. Parameters with high sensitivities require fine-tuning to achieve optimality. [ABSTRACT FROM AUTHOR]

• A novel optimizer called Chimp Optimization Algorithm (ChOA) is proposed. • ChOA is inspired by individual intelligence and sexual motivation of chimps. • ChOA alleviates the problems of slow convergence rate and trapping in local optima. • The four main steps of Chimp hunting are implemented. This paper proposes a novel metaheuristic algorithm called Chimp Optimization Algorithm (ChOA) inspired by the individual intelligence and sexual motivation of chimps in their group hunting, which is different from the other social predators. ChOA is designed to further alleviate the two problems of slow convergence speed and trapping in local optima in solving high-dimensional problems. In this paper, a mathematical model of diverse intelligence and sexual motivation of chimps is proposed. In this regard, four types of chimps entitled attacker, barrier, chaser, and driver are employed for simulating the diverse intelligence. Moreover, four main steps of hunting, i.e. driving, chasing, blocking, and attacking, are implemented. The proposed ChOA algorithm is evaluated in 3 main phases. First, a set of 30 mathematical benchmark functions is utilized to investigate various characteristics of ChOA. Secondly, ChOA was tested by 13 high-dimensional test problems. Finally, 10 real-world optimization problems were used to evaluate the performance of ChOA. The results are compared to several newly proposed meta-heuristic algorithms in terms of convergence speed, the probability of getting stuck in local minimums, and exploration, exploitation. Also, statistical tests were employed to investigate the significance of the results. The results indicate that the ChOA outperforms the other benchmark optimization algorithms. [ABSTRACT FROM AUTHOR]

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

Sine Cosine Algorithm (SCA) that is one of the population-based metaheuristic optimization algorithms basically consists of the updating mechanism based on sine and cosine functions. In this algorithm, a few random and adaptive variables are also utilized for more effective motions of the candidate solutions. SCA has some drawbacks like other some metaheuristic algorithms. SCA tends to be stuck into the local regions in the search space and this affects negatively on the computational effort required to find the best solution point in the search space. This paper presents four different improved versions of SCA. The proposed improvements on original SCA are the innovations on the updating mechanism of SCA. To evaluate the performances of Improved Sine Cosine Algorithms (ImpSCAs), well-known numerical optimization problems including CEC 2014 test suite are used. Firstly, different analyses of the proposed ImpSCAs are dealt with such as the convergence analysis, search history analysis, trajectory analysis, average distance analysis, and computational complexity analysis. Secondly, the proposed four versions of ImpSCAs are compared with the original SCA for CEC 2014 benchmark problems with dimension sizes of 10D, 30D and 50D. Finally, original SCA and ImpSCAs are adapted to select optimal feature combination and they are tested for 10 feature selection datasets taken from the UCI machine learning repository. The benchmark results show that the performances of the ImpSCA 1 , ImpSCA 2 , and ImpSCA 4 are better than that of the original SCA. From the feature selection results, it is observed that three versions of ImpSCAs (except ImpSCA 3) outperform the original SCA in 80% of the datasets. Source codes of ImpSCAs are publicly available at https://github.com/uguryuzgec/ImpSCAs. • We focused to improve the exploration and exploitation ability of the original SCA. • The basic idea is based on the innovations on the update mechanism. • The ISCAs were evaluated using CEC2014 benchmark test suite. • The proposed ISCAs were tested for 10 popular UCI datasets. • Three versions of ISCAs (except ISCA 3) outperform the SCA in 80% of the datasets. [ABSTRACT FROM AUTHOR]

The moth-flame optimization (MFO) algorithm is an effective nature-inspired algorithm based on the chemical effect of light on moths as an animal with bilateral symmetry. Although it is widely used to solve different optimization problems, its movement strategy affects the convergence and the balance between exploration and exploitation when dealing with complex problems. Since movement strategies significantly affect the performance of algorithms, the use of multi-search strategies can enhance their ability and effectiveness to solve different optimization problems. In this paper, we propose a multi-trial vector-based moth-flame optimization (MTV-MFO) algorithm. In the proposed algorithm, the MFO movement strategy is substituted by the multi-trial vector (MTV) approach to use a combination of different movement strategies, each of which is adjusted to accomplish a particular behavior. The proposed MTV-MFO algorithm uses three different search strategies to enhance the global search ability, maintain the balance between exploration and exploitation, and prevent the original MFO's premature convergence during the optimization process. Furthermore, the MTV-MFO algorithm uses the knowledge of inferior moths preserved in two archives to prevent premature convergence and avoid local optima. The performance of the MTV-MFO algorithm was evaluated using 29 benchmark problems taken from the CEC 2018 competition on real parameter optimization. The gained results were compared with eight metaheuristic algorithms. The comparison of results shows that the MTV-MFO algorithm is able to provide competitive and superior results to the compared algorithms in terms of accuracy and convergence rate. Moreover, a statistical analysis of the MTV-MFO algorithm and other compared algorithms was conducted, and the effectiveness of our proposed algorithm was also demonstrated experimentally. [ABSTRACT FROM AUTHOR]

It is always desirable to keep the number of components in compositional simulation low due to the high CPU time and storage space requirement; on the other hand, the model's accuracy in describing the phase behavior decreases with reducing the number of components. As a result, an optimal lumping approach is needed to group the components to attain high accuracy in the least amount of time. This paper's main objective is to find the best lumping scheme to maintain the split model's accuracy and reach an optimal lumped model in the least amount of time. In accordance with this purpose, five different evolutionary-based metaheuristic optimization algorithms known as genetic algorithm (GA), differential evolution (DE), harmony search (HS), imperialist competitive algorithm (ICA), and shuffled-frog leaping algorithm (SFLA) are incorporated into the objective function developed to cluster heavy fractions using the k-means algorithm. To find the optimal algorithm, the best cost of the objective function and the number of function evaluations (NFE) have been compared, and the final results were substantiated using flash calculation outputs, such as density, viscosity, phase diagram, and Wilson plot. The results indicate that the proposed algorithms are capable of finding the optimal lumped model with high accuracy for all reservoir samples tested in this study. This study also marked ICA as the best evolutionary-based optimization algorithm that can be used in clustering problems. • Clustering approach using Evolutionary-based algorithms to lump heavy-end component. • The order of algorithms efficiency to lump is ICA > SFLA > GA ≈ DE ≫ HS. • ICA and SFLA attain the best cost in the lowest number of function evaluation. • The efficiency of ICA does not reduce with the increase in the number of components. • 16 components are adequate enough to present ICA-16 as the optimum lumping strategy. [ABSTRACT FROM AUTHOR]

This paper introduces a novel population-based bio-inspired meta-heuristic optimization algorithm, called Blood Coagulation Algorithm (BCA). BCA derives inspiration from the process of blood coagulation in the human body. The underlying concepts and ideas behind the proposed algorithm are the cooperative behavior of thrombocytes and their intelligent strategy of clot formation. These behaviors are modeled and utilized to underscore intensification and diversification in a given search space. A comparison with various state-of-the-art meta-heuristic algorithms over a test suite of 23 renowned benchmark functions reflects the efficiency of BCA. An extensive investigation is conducted to analyze the performance, convergence behavior and computational complexity of BCA. The comparative study and statistical test analysis demonstrate that BCA offers very competitive and statistically significant results compared to other eminent meta-heuristic algorithms. Experimental results also show the consistent performance of BCA in high dimensional search spaces. Furthermore, we demonstrate the applicability of BCA on real-world applications by solving several real-life engineering problems. [ABSTRACT FROM AUTHOR]

Evolutionary computation-based metaheuristic algorithms have been successfully applied to hard optimization problems. In this very active research area, one of the newest algorithms is a Cuckoo search metaheuristic capable of solving general Ndimensional, linear and nonlinear optimization problems. It is easy to understand and apply, and requires simple mathematical preprocessing. Ever since its foundation in 2009, Cuckoo search algorithm has drawn the attention of many researchers all over the world, resulting in a lot of variants of the basic algorithm with improved performance. This paper reviews the basic concepts of Cuckoo search algorithm, a survey of its major variants applied to solve diverse optimization problems in engineering sciences and the theoretical studies conducted on it so far. Also, it provides an overview of the significant engineering applications that have benefited from its powerful nature. [ABSTRACT FROM AUTHOR]

Metaheuristics are an important branch of optimization algorithms that attract lots of research and application efforts. In this paper, the research of predicting solution value for Nested Partitions (NP) is proposed, which is a newly developed metaheuristic algorithm for solving large-scale optimization problems. The lower bound embedded prediction procedures are developed to predict the future performance of NP based on the solution values obtained at early iterations. The prediction procedures are used in an enhanced NP algorithm to select a proper algorithm setting for NP at early stage, which saves a lot of computational resource for large-scale problems. The computational tests show the accuracy and effectiveness of the proposed algorithm. These prediction procedures can be also applied to some other metaheuristics. [ABSTRACT FROM AUTHOR]

In this paper, a discrete moth–flame optimization algorithm for community detection (DMFO-CD) is proposed. The representation of solution vectors, initialization, and movement strategy of the continuous moth–flame optimization are purposely adapted in DMFO-CD such that it can solve the discrete community detection. In this adaptation, locus-based adjacency representation is used to represent the position of moths and flames, and the initialization process is performed by considering the community structure and the relation between nodes without the need of any knowledge about the number of communities. Solution vectors are updated by the adapted movement strategy using a single-point crossover to distance imitating, a two-point crossover to calculate the movement, and a single-point neighbor-based mutation that can enhance the exploration and balance exploration and exploitation. The fitness function is also defined based on modularity. The performance of DMFO-CD was evaluated on eleven real-world networks, and the obtained results were compared with five well-known algorithms in community detection, including GA-Net, DPSO-PDM, GACD, EGACD, and DECS in terms of modularity, NMI, and the number of detected communities. Additionally, the obtained results were statistically analyzed by the Wilcoxon signed-rank and Friedman tests. In the comparison with other comparative algorithms, the results show that the proposed DMFO-CD is competitive to detect the correct number of communities with high modularity. [ABSTRACT FROM AUTHOR]

Since nature is an excellent source of inspiration for optimization methods, many optimization algorithms have been proposed, are inspired by nature, and are modified to solve various optimization problems. This paper uses metaheuristics in a new field inspired by nature; more precisely, we use pollination optimization in cocoa plants. The cocoa plant was chosen as the object since its flower type differs from other kinds of flowers, for example, by using cross-pollination. This complex relationship between plants and pollinators also renders pollination a real-world problem for chocolate production. Therefore, this study first identified the underlying optimization problem as a deferred fitness problem, where the quality of a potential solution cannot be immediately determined. Then, the study investigates how metaheuristic algorithms derived from three well-known techniques perform when applied to the flower pollination problem. The three techniques examined here are Swarm Intelligence Algorithms, Individual Random Search, and Multi-Agent Systems search. We then compare the behavior of these various search methods based on the results of pollination simulations. The criteria are the number of pollinated flowers for the trees and the amount and fairness of nectar pickup for the pollinator. Our results show that Multi-Agent System performs notably better than other methods. The result of this study are insights into the co-evolution of behaviors for the collaborative pollination task. We also foresee that this investigation can also help farmers increase chocolate production by developing methods to attract and promote pollinators. [ABSTRACT FROM AUTHOR]

A multitude of optimization tasks ensue in the context of the smart grid, often exhibiting undesirable characteristics like non-convexity, mixed types of design variables and multiple - and often conflicting - objectives. These tasks can be broadly categorized into three classes of problems, namely optimal power flow (OPF), scheduling and planning. Metaheuristic search methods form a generic class of optimization techniques, that have been shown to work successfully for complex problems. Not surprisingly, they have been widely applied in the smart grid, their use spanning almost every smart grid-related optimization task. In this work, we review the use of metaheuristic search for OPF, scheduling and planning through a unified approach, keeping in mind that these problems share many common challenges and objectives. The use of different metaheuristic methods is discussed extensively with regard to problem handling, multi-objective optimization performance and method accuracy in relation to computational complexity. An attempt to arrive at quantitative conclusions is also being made, by compiling tables which present collective results on common test grids. Lastly, the paper identifies promising directions for future research, concerning metaheuristic search application practices, method development and new challenges that we believe will shape the future of smart grid optimization. • We review the use of metaheuristic search methods for optimization in the smart grid. • Common challenges and objectives for OPF, scheduling and planning are identified. • We compile comparative tables presenting collective results on common test grids. • Conclusions are drawn on problem handling, accuracy, complexity and MOO performance. • We identify future directions on method development and use, to face emerging challenges. [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]

• A new optimization algorithm based on Lichtenberg figure pattern is presented. • Difussion Limited Aggregation is formulated to create new agents in search space. • Lichtenberg figure pattern presents exploration–exploitation balance. • Tested on complex functions and problems and compared with renowned metaheuristics. This paper proposes a novel global optimization algorithm called Lichtenberg Algorithm (LA), inspired by the Lichtenberg figures patterns. Optimization is an essential tool to minimize or maximize functions, obtaining optimal results on costs, mass, energy, gains, among others. Actual problems may be multimodal, nonlinear, and discontinuous and may not be minimized by classical analytical methods that depend on the gradient. In this context there are metaheuristics algorithms inspired by natural phenomena to optimize real problems. There is no algorithm that is the worst or the best, but more efficient for a given type of problem. Thus, an unprecedented metaheuristic algorithm was created inspired by the physical phenomenon of radial intra-cloud lightning and Lichtenberg figures, successfully exploiting the fractal power and it is different from many in the literature as it is a hybrid algorithm composed of methods of search based on population and trajectory. Several test functions, including a design problem in a welded beam, were used to verify the robustness and to validate the Lichtenberg Algorithm. In all cases, the results were satisfactory when compared to those in the literature. LA shown to be a powerful optimization tool for both unconstraint optimizations and real problems with linear and nonlinear constraints. [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]

• PID controller is used to power control of a PWR-type nuclear power plant. • GA and PSO are used for PID gains tuning based on the performance index of ITSE. • Algorithms are compared in a number of function evaluations (NFE). • The robustness of the controller is investigated with parametric uncertainties. • PSO gets the optimal answer with a lower computational effort (NFE) compared to GA. Power level control is important in nuclear power plants, and some dynamic parameters depend on the output power. One of the popular controllers is proportional–integral–derivative (PID), that the researchers are interested in tuning its gains. In this paper, computational effort comparison of genetic algorithm (GA) and particle swarm optimization (PSO) algorithm is aimed to tune the PID gains for the typical pressurized water reactor (PWR) load-following, based on the point kinetics model with and without the parametric uncertainties. The integral of time-weighted square error (ITSE) performance index is used in the GA and PSO metaheuristic algorithms. The number of function evaluations (NFE) is used for the computational effort comparison as the novelty of this work. The results of both optimization methods show the high performance of the closed-loop metaheuristic–PID controller for the desired load-following with robustness to the parametric uncertainties. However, PSO achieves the optimal solution in less NFE. [ABSTRACT FROM AUTHOR]

Pipe design is one of the most significant research lines in the area of parabolic semi-cylindrical solar collectors. The main idea behind pipe design is to increase the capillarity angle by expanding the total area being heated, therefore boosting the work capacity of the device. Such capillarity depends on several factors, whose numerical calculations are highly complex. Moreover, some of those variables are integers, whereas some others are real; hence, it is necessary to use optimization techniques that are capable of searching in those numerical spaces. There are several optimization tools that allow individual codification as binary strings, granting the coding of integer, real, or any other, as part of the same individual. Consequently, in this paper we propose the comparison of four metaheuristics when they are utilized to maximize the capillarity angle of the pipe in a parabolic trough. Experimental results show a better performance of binary particle swarm optimization when compared against the other techniques, achieving improvements in the capillarity angle of on average 11 % in comparison with a similar study. [ABSTRACT FROM AUTHOR]