Search

Several optimization-based population search methods have been proposed; they use various operators that permit exploring the search space. These methods typically suffer from local search (LS) problems and are unbalanced between exploration and exploitation. Consequently, recent researchers sought to modify the algorithms to avoid search problems using local search techniques to intensify the exploitation when is necessary. This paper proposes a novel single-based local search optimization algorithm called the non-monopolize search (NO). The NO is a single-solution metaphor-free algorithm, and its operators are designed based to explore and exploit along the iterative process. The NO works only with a candidate solution, and the operators modify the dimension to move the current solution along the search space. The NO is an effective LS method that combines the benefits of exploration with exploitation. Different from other LS, the NO can escape from suboptimal solutions thanks to the randomness incorporated into its operators. This is the main advantage of the NO. Experiments are conducted on standard benchmark functions to validate the performance of the proposed non-monopolize search optimization technique. The results are compared with other well-known methods, and the proposed NO got better results. Moreover, the proposed NO can be considered a powerful alternative to improve the optimization algorithms' performance and help avoid local search problems. Source codes of NO are publicly available at https://www.mathworks.com/matlabcentral/fileexchange/156154-the-non-monopolize-search-no. [ABSTRACT FROM AUTHOR]

Vortex search (VS) algorithm is a recently proposed swarm intelligence or evolutionary algorithm for solving continuous optimization problems inspired by the behavior of whirlpool. In this study, an approach based on VS algorithm is proposed to deal with uncapacitated facility location problem (UFLP) which is a pure problem in binary domain. The update mechanism of VS algorithm is not sufficiently useful for solving the binary optimization problems; therefore, a binary form of VS method called modified binary vortex search (for short MBVS) is proposed for solving UFLPs. Three important changes have been carried out on basic VS algorithm such as (1) converting continuous values to binary values; (2) using genetic mutation operators for enhancing the exploration ability and (3) a local search mechanism for extending the exploitation ability. Based on these changes, MBVS has been tested on fifteen different UFLP instances. The UFLPs dealt with in this study is one of the famous binary optimization problems. It is widely used for comparing the performance of superior algorithms. Once an analysis of 10 different transfer functions, a genetic mutation operator, a local search parameter and population size have been made on proposed method; then, it has been compared with some binary metaheuristic methods and their variants: genetic algorithm (GA)-based approaches such as GA-SP, GA-TP and GA-UP; binary particle swarm optimization algorithm (BPSO); binary versions of artificial bee colony (ABC) algorithm such as binABC, DisABC and ABCbin; binary versions of differential evolution (DE) algorithm such as DisDe/rand and binDE and binary variants of the artificial algae algorithm (AAA) such as AAA-Tanh, AAA-Sig and binAAA methods. The experimental results and comparisons reveal that MBVS algorithm is highly competitive and robust optimizer for the problem addressed in this study. [ABSTRACT FROM AUTHOR]

A population-based metaheuristic algorithm that takes its cues from the foraging strategy of sparrows is called the sparrow search algorithm (SSA). While SSA is competitive when compared to other algorithms, it nevertheless has a propensity to carry out imbalanced exploitation and exploration and find the local optimum. Therefore, the modified adaptive sparrow search algorithm (MASSA), an SSA modification, is created to address these problems. To increase population variety, the MASSA uses a chaotic reverse learning technique. Second, to balance the exploitation and exploration capacities, a dynamic adaptive weight is added. In the end, an adaptive spiral search technique improves algorithm performance. Among 23 classical test functions, of which 13 are multidimensional and the other 10 are fixed dimensional, the best chaotic operator is found. It is proven that MASSA is superior. Simulation studies demonstrate that the MASSA described in this study is superior to previous algorithms in terms of stability, convergence speed, and convergence accuracy. Finally, a sample robot path planning problem is resolved using MASSA, and the experimental outcomes confirmed the viability and usefulness of MASSA. [ABSTRACT FROM AUTHOR]

This paper introduces a new variant of a recent metaheuristic algorithm based on the Social Network Search algorithm (SNS), which is called the Roulette Wheel Social Network Search algorithm (SNS). As the name indicates, the main feature of RWSNS is the fact that the algorithm allows proportionate selection of its search operators (i.e., from imitation, conversation, disputation and innovation) through exploiting the roulette wheel. Additionally, RWSNS also incorporates the Piecewise map as replacement for the pseudo random generator during the population initialisation to ensure high nonlinearity and allow further solution diversification. Finally, unlike its predecessor, RWSNS also permits the systematic manipulation of candidate solutions around the global best agent through the swap operator to boost its search intensification process, as the global best candidate solution is often clustered and always lurking around the current local best. Results based on the construction of 8 × 8 substitution-box demonstrate that the proposed RWSNS exceeds other competing metaheuristic algorithms in two main S-box criteria, namely, the average nonlinearity score and strict avalanche criteria (i.e., SAC offset), whilst maintaining a commendable performance on bits independence criteria, differential approximation probability and linear approximation probability. [ABSTRACT FROM AUTHOR]

This paper introduces a novel population-based vortex search algorithm (PVS) to improve the weakness of the global search capability of the original Vortex Search (VS) algorithm which is a simple and efficient physics-based metaheuristic and originally has a single-solution-based structure. Single-solution-based metaheuristics perform a local search in the neighborhood of a single solution, whereas population-based metaheuristics conduct the search process by creating several candidate solutions at different points in the search space. The fast-running structures of single-solution-based algorithms may cause the search process to get stuck in the local optimum in some cases. The proposed algorithm transforms VS into a population-based structure with a location update operator and the polynomial mutation operator. Also, the strategy of generating solutions based on the radius reduction mechanism around a center is maintained. Furthermore, two variants of PVS, called PVS_a and PVS_b, are presented in this study. The performance of the proposed approach is investigated by applying a set of experimental series. Three different benchmark sets involving (i) 20 classical benchmark functions, (ii) 29 CEC2017 test functions, and (iii) 10 CEC2019 test functions are employed in experiments. In addition, four real-world-constrained optimization problems are used to evaluate the effectiveness of PVS. Considering the experimental results obtained from the comparison of the proposed algorithm with both state-of-the-art and recent metaheuristics, the developed PVS_a algorithm provides highly quite promising and superior outcomes in solution quality and robustness. [ABSTRACT FROM AUTHOR]

The minimum vertex cover problem (MVCP) is one of the well-known NP-complete problems that can be used to formulate numerous real-life applications. The MVCP has been solved using different approaches, exact, heuristic, and metaheuristic. Chemical reaction optimization (CRO) is a population-based metaheuristic algorithm that simulates what happens in chemical reactions to solve many problems. In this paper, the MVCP is solved using hybridization of a modified version of the CRO algorithm and the best-first search (BFS) algorithm. This hybridization is symbolized by MCROA-BFS. The BFS algorithm is exploited to generate initial populations with high-quality initial solutions in comparison with the random bit-vector (RBV) approach as a traditional approach for generating initial populations for several population-based metaheuristic algorithms. At first, the MCROA is evaluated analytically in terms of run time complexity. Then the performance of the MCROA is evaluated experimentally to show the effectiveness of exploiting the BFS in terms of quality of gained solutions and run time. The most valuable player algorithm (MVPA), genetic algorithm (GA), and hybrid CRO algorithm (HCROA) are metaheuristic algorithms that used the RBV approach to generate the initial population. MCROA-BFS is compared, under the selected performance metrics, with these metaheuristic algorithms in addition to MCROA with the RBV approach (MCROA-RBV). The conducted experiments were carried out using 18 instances of DIMACS and BHOSLIB benchmarks. The experimental results revealed the superiority of MCROA-BFS over MVPA, GA, and MCROA-RBV in terms of both performance metrics. Although HCROA has the smallest run time, it failed to obtain high-quality solutions in comparison with MCROA. [ABSTRACT FROM AUTHOR]

Feature Selection (FS) is an important preprocessing step that is involved in machine learning and data mining tasks for preparing data (especially high-dimensional data) by eliminating irrelevant and redundant features, thus reducing the potential curse of dimensionality of a given large dataset. Consequently, FS is arguably a combinatorial NP-hard problem in which the computational time increases exponentially with an increase in problem complexity. To tackle such a problem type, meta-heuristic techniques have been opted by an increasing number of scholars. Herein, a novel meta-heuristic algorithm, called Sparrow Search Algorithm (SSA), is presented. The SSA still performs poorly on exploratory behavior and exploration-exploitation trade-off because it does not duly stimulate the search within feasible regions, and the exploitation process suffers noticeable stagnation. Therefore, we improve SSA by adopting: i) a strategy for Random Re-positioning of Roaming Agents (3RA); and ii) a novel Local Search Algorithm (LSA), which are algorithmically incorporated into the original SSA structure. To the FS problem, SSA is improved and cloned as a binary variant, namely, the improved Binary SSA (iBSSA), which would strive to select the optimal or near-optimal features from a given dataset while keeping the classification accuracy maximized. For binary conversion, the iBSSA was primarily validated against nine common S-shaped and V-shaped Transfer Functions (TFs), thus producing nine iBSSA variants. To verify the robustness of these variants, three well-known classification techniques, including k-Nearest Neighbor (k-NN), Support Vector Machine (SVM), and Random Forest (RF) were adopted as fitness evaluators with the proposed iBSSA approach and many other competing algorithms, on 18 multifaceted, multi-scale benchmark datasets from the University of California Irvine (UCI) data repository. Then, the overall best-performing iBSSA variant for each of the three classifiers was compared with binary variants of 12 different well-known meta-heuristic algorithms, including the original SSA (BSSA), Artificial Bee Colony (BABC), Particle Swarm Optimization (BPSO), Bat Algorithm (BBA), Grey Wolf Optimization (BGWO), Whale Optimization Algorithm (BWOA), Grasshopper Optimization Algorithm (BGOA) SailFish Optimizer (BSFO), Harris Hawks Optimization (BHHO), Bird Swarm Algorithm (BBSA), Atom Search Optimization (BASO), and Henry Gas Solubility Optimization (BHGSO). Based on a Wilcoxon's non-parametric statistical test ( α = 0.05 ), the superiority of iBSSA with the three classifiers was very evident against counterparts across the vast majority of the selected datasets, achieving a feature size reduction of up to 92% along with up to 100% classification accuracy on some of those datasets. [ABSTRACT FROM AUTHOR]

Due to the nonlinear characteristic of the power-voltage (P–V) and current–voltage (I–V) relationship of the photovoltaic systems, building accurate mathematical models of photovoltaic cell and module is essential for validation and optimization performance of photovoltaic systems. However, determination of the unknown parameters of photovoltaic cell and module models is a complex nonlinear optimization problem that requires effective solving methods. This paper proposes an approach of parameter estimation of photovoltaic cell and module models based on artificial ecosystem-based optimization (AEO). The AEO is a new developed metaheuristic algorithm taken idea of the mechanisms of ecosystem consisting of production, consumption and decomposition. The advanced feature of AEO is the diversity of exploration and exploitation mechanisms without special control parameters. The performance of AEO is discussed in two photovoltaic cell models and two photovoltaic module models. Its performances compared to five algorithms consisting of backtracking search algorithm (BSA), cuckoo search algorithm (CSA), equilibrium optimizer (EO), genetic algorithm (GA) and particle swarm optimization (PSO) as well as the previous methods. The numerical results show that AEO has given the better performance than BSA, CSA, EO, GA and PSO for the problem of parameter estimation of photovoltaic models in terms of aspects such as reaching the lower error between the experimental and estimated values and having the better statistical results in several runs than the above methods. Thus, AEO can be an effective method for estimating the unknown parameters of photovoltaic cell and module models. [ABSTRACT FROM AUTHOR]

Power system stabilizers (PSSs) are extensively used in generator units to enhance the transient stability of the power system. Hence, optimal tuning and placement of the parameters of PSS are crucial for the efficiency of the stabilizer. Researchers have been proposed many methods for optimizing such parameters. In this paper, the Crow search algorithm (CSA), which is based on the intelligence of crows, was employed in a single-machine infinite-bus (SMIB) system to determine the optimum parameters of the PSS. Modeling and simulation of the SMIB and designing of PSS were made by MATLAB/Simulink. PSSs are designed to minimize low-frequency oscillations such as power angle, rotor speed, and field current deviation following a large disturbance. Our objective in this study is the minimization of the rotor speed deviation. The results of the simulations proved the effectiveness and robustness of the optimization process compared to other metaheuristic algorithms such as particle swarm optimization (PSO) and genetic algorithm (GA). When compared to the performance attained by the GA-based and PSO-based PSS controller designs, the simulations show that the CSA-based PSS delivers a far better dynamic response when the system is disrupted. The CSA-based PSS settles 48.1% faster than the PSO-based PSS and 55.7% faster than the GA-based PSS. CSA has just 2 parameters to adjust, making it much easier to implement than other methods. These parameters for PSO and GA are 4 and 6. When CSA-based PSS is used in the SMIB system, overshoot and low-frequency oscillations are also significantly reduced compared to other methods. [ABSTRACT FROM AUTHOR]

Feature extraction, feature clustering, feature selection are suitable to enhance learning performance, reduce computational complexity, create better generalizable models, and reduce required storage. Although there are several feature reduction techniques, still it remains one of the research hot spots in the field of data mining and machine learning groups. Owl search algorithm (OSA) is one of the recent metaheuristic optimization algorithms that mimic the hunting mechanism of owls in dark. However, OSA suffers from the same problem faced by many other optimization algorithms and tends to fall in local optima and premature convergence. To overcome these problems, two improvements for OSA algorithm are proposed in this paper. The first improvement uses a fuzzy system which is responsible to tune a control parameter in the updating phase of OSA. The second improvement includes the fuzzy system along with modifying the updating equation of OSA to enhance the exploration activity. In addition, this paper presents a new feature extraction technique for regression problems based on the improved OSA, called Fuzzy Owl Clustering Dimension Reduction (FOCDR). We apply a method that uses three weighting methods (i.e., soft, hard, and mixed) to extract new features based on the generated clusters. The experiment is divided into two parts. In the first part, the performance of the OSA algorithm and two improvement versions are analyzed with ten benchmark functions. The results show that the proposed versions on average can improve the convergence rate by 6.75% and 14.2% compared to OSA in solving complex problems. The second part is conducted to show FOCDR's ability for feature selection problems. The effectiveness of FOCDR has been evaluated using four benchmark datasets and a real-world case study. [ABSTRACT FROM AUTHOR]

In this article, a new metaheuristic algorithm named average differential evolution with local search (ADE-LS) has been developed and implemented to find the optimal coefficients of unknown infinite impulse response (IIR) system as a system identifier. The developed method minimizes the error between unknown system output and the adaptive IIR filter output. Rapid convergence is aimed for the global solution in system identification problem using the ADE-LS based adaptive IIR filter modelling with local search. In this way, more precise prediction of filter coefficients is ensured in the filter design with multimodal error surface. ADE-LS algorithm is applied to four benchmarked IIR systems commonly studies in literature to show its performance. Results found by using ADE-LS are compared to other methods reported in terms of convergence rate and the mean square error value. The attained results approve the efficiency of the suggested method. [ABSTRACT FROM AUTHOR]

The traffic light cycle optimization problem (TLCOP) is certainly one of the most critical problems in a modern traffic management system because a "good solution" will be able to reduce the total waiting time of vehicles on roads of an entire city. In addition to heuristic algorithms, metaheuristic algorithms provide an alternative way for solving this optimization problem in the sense that they provide a better solution to adjust the traffic lights to mitigate the traffic congestion problem. However, there is still plenty of room for improvement. One of the open issues is that most metaheuristic algorithms will converge to a few regions at the later stage of the convergence process and thus are likely to fall into local optima. The proposed algorithm—a hybrid heuristic algorithm called grey wolf with grasshopper optimization (GWGO)—is developed to leverage the strength of grey wolf and grasshopper optimization algorithms. The underlying idea is to use the grey wolf optimization to avoid falling into local optimum too quickly while using the grasshopper optimization to dynamically adjust the convergence speed of the search algorithm. The experimental results show that the proposed algorithm is able to find out better results than all the other state-of-the-art search algorithms for the TLCOP evaluated in this study in terms of the quality of the end results. [ABSTRACT FROM AUTHOR]

Cuckoo search algorithm is one of the most prominent meta-heuristic optimization algorithms which is applied to various applications. The discovery probability is the one and the only tuning parameter of the cuckoo search algorithm. The physical meaning of this parameter contradicts its implementation in the standard algorithm. Therefore, this study concerns the correction to the definition and implementation of the cuckoo search algorithm to resolve this conflict. Moreover, a novel algorithm called double exponential cuckoo search is proposed, in which the discovery probability became adaptive based on the concept of the double Mersenne numbers. The proposed algorithm is compared to nine other variants to find the best variant that makes the discovery probability adaptive. All the variants are compared and tested on 30 and 50 dimensions of CEC2017 benchmark functions. The results have been statistically proved using the sign test, Wilcoxon signed-rank test, and Friedman test. Moreover, multiple graphical methods are also used to visualize the median performance such as Violin plots and mean convergence graphs. Simulation results prove the superior performance of the proposed algorithm over all other variants. [ABSTRACT FROM AUTHOR]

This paper discusses a new variant of Henry Gas Solubility Optimization (HGSO) Algorithm, called Hybrid HGSO (HHGSO). Unlike its predecessor, HHGSO allows multiple clusters serving different individual meta-heuristic algorithms (i.e., with its own defined parameters and local best) to coexist within the same population. Exploiting the dynamic cluster-to-algorithm mapping via penalized and reward model with adaptive switching factor, HHGSO offers a novel approach for meta-heuristic hybridization consisting of Jaya Algorithm, Sooty Tern Optimization Algorithm, Butterfly Optimization Algorithm, and Owl Search Algorithm, respectively. The acquired results from the selected two case studies (i.e., involving team formation problem and combinatorial test suite generation) indicate that the hybridization has notably improved the performance of HGSO and gives superior performance against other competing meta-heuristic and hyper-heuristic algorithms. [ABSTRACT FROM AUTHOR]

In this paper, two optimal operation plans of a cascaded reservoir system with four hydropower plants are studied for reaching the highest power energy of all the power plants. In the first plan, called local optimization plan, optimal water discharge of each upstream hydropower plant is determined first and the optimal discharge is then used as input data for determining optimal water discharge of other downstream hydropower plants. In the second plan, called global optimization plan, optimal water discharge of all hydropower plants is simultaneously determined. For reaching the highest total power energy of the system, nine methods consisting of moth swarm algorithm, Harris hawks optimization, cuckoo search algorithm (CSA), snap drift cuckoo search algorithm (SD-CSA), stochastic fractal search algorithm, parasitism predation algorithm, marine predator algorithm, tunicate swarm algorithm, and the proposed improved cuckoo search algorithm (ICSA) are implemented. Among the methods, the proposed ICSA is first developed by improving the exploitation phase of CSA. The comparisons of power energy from each hydropower plant for the two plans indicate that local optimization plan is more useful for upstream plants but for downstream plants, whereas global optimization plan is more useful for downstream plants. For the purpose of reaching the highest total power energy for the whole system, the global optimization plan is more effective because it reaches higher power energy than local optimization plan. For investigating the improvement of the proposed method over CSA and SD-CSA, the three methods are run for seven benchmark functions and for the global optimization plan with different population sizes and different iteration numbers. As a result, it is recommended that global optimization plan should be applied for all hydropower plants in cascaded systems and downstream plants with higher power energy can share benefit to upstream ones. In addition, one of the most effective methods that should be recommended for implementing global optimization plan is the proposed ICSA. [ABSTRACT FROM AUTHOR]

The backpropagation (BP) algorithm is a gradient-based algorithm used for training a feedforward neural network (FNN). Despite the fact that BP is still used today when FNNs are trained, it has some disadvantages, including the following: (i) it fails when non-differentiable functions are addressed, (ii) it can become trapped in local minima, and (iii) it has slow convergence. In order to solve some of these problems, metaheuristic algorithms have been used to train FNN. Although they have good exploration skills, they are not as good as gradient-based algorithms at exploitation tasks. The main contribution of this article lies in its application of novel memetic approaches based on the Gravitational Search Algorithm (GSA) and Chaotic Gravitational Search Algorithm (CGSA) algorithms, called respectively Memetic Gravitational Search Algorithm (MGSA) and Memetic Chaotic Gravitational Search Algorithm (MCGSA), to train FNNs in three classical benchmark problems: the XOR problem, the approximation of a continuous function, and classification tasks. The results show that both approaches constitute suitable alternatives for training FNNs, even improving on the performance of other state-of-the-art metaheuristic algorithms such as ParticleSwarm Optimization (PSO), the Genetic Algorithm (GA), the Adaptive Differential Evolution algorithm with Repaired crossover rate (Rcr-JADE), and the Covariance matrix learning and Bimodal distribution parameter setting Differential Evolution (COBIDE) algorithm. Swarm optimization, the genetic algorithm, the adaptive differential evolution algorithm with repaired crossover rate, and the covariance matrix learning and bimodal distribution parameter setting differential evolution algorithm. [ABSTRACT FROM AUTHOR]

Cloud manufacturing as an emerging service-oriented manufacturing paradigm integrates and manages geographically distributed manufacturing resources such that complex and highly customized manufacturing tasks can be performed cooperatively. The service composition and optimal selection (SCOS) problem, in which manufacturing cloud services are optimally selected for performing subtasks, is one of the key issues for implementing a cloud manufacturing system. In this paper, we propose a new mixed-integer programming model for solving the SCOS problem with sequential composition structure. Unlike the majority of previous research on the problem, in the proposed model, the transportation between distributed resources and its effects on quality of services are considered. Although a wide variety of metaheuristics have been tailored for solving the SCOS problem, no consistent and comprehensive conclusion has been reached so far on the superiority of a specific algorithm. Therefore, for the first time, solution space landscape of the problem was analyzed through several statistical criteria which demonstrated that the landscape is rugged and local optima are clustered in a small region of the search space. Therefore, to find good solutions, a metaheuristic algorithm needs to perform both proper exploitation and exploration of the search space. According to the landscape analysis, the basic imperialist competitive algorithm (ICA) was hybridized with a local search (LS) algorithm resulting in the hybrid ICA (HICA). To examine the performance of the proposed HICA, an example of online motorcycle production in the USA as well as four randomly generated large-scale instances, were solved through the LS, ICA, and HICA. Computational results showed that transportation consideration is indispensable for obtaining more realistic solutions in cloud manufacturing. The results also revealed that the HICA outperformed the LS and basic ICA in terms of the value of cost objective function, the stability of solutions and convergence speed. Hence, not only statistically but also analytically, it was proved that algorithms incorporating both exploitation and exploration are able to solve the SCOS problem more efficiently. [ABSTRACT FROM AUTHOR]

For the purpose of usability feature extraction and prediction, an innovative metaheuristic algorithm is introduced. Generally, the term "usability" is defined by the several researchers with respect to the hierarchical-based software usability model and it has become one of the important methods in terms of software quality. In hierarchically based software, its usability factors, attributes, and its characteristics are combined. The paper presented an algorithm, i.e., modified crow search algorithm (MCSA) mainly for extraction of usability features from hierarchical model with the optimal solution under the search for useful features. MCSA is an extension of original crow search algorithm (CSA), which is a naturally inspired algorithm. The mechanism of this algorithm is based on the process of hiding food and prevents theft and hence introduced this CSA in the field of software engineering practices as an inspiration. The algorithm generates a particular number of selected features/attributes and is applied on software development life cycles models, finding out the best among them. The results of the presented algorithm are compared with the standard binary bat algorithm (BBA), original CSA, and modified whale optimization algorithm (MWOA). The outcomes conclude that the proposed MCSA performs well than the standard BBA and original CSA as the proposed algorithms generate fewer number of feature selection equal to 17 than 18 in BBA, 23 in CSA, and 19 in MWOA. [ABSTRACT FROM AUTHOR]

This study proposes a novel strength of multi-objective gravitational search algorithm and bat algorithm MOGSABAT to solve multi-objective optimisation problem. The proposed MOGSABAT algorithm is divided into three stages. In the first stage (moving space), a switch in a solution from single function to multiple functions that contain more than one objective to use the gravitational search algorithm GSA is determined. We established a new equation to calculate the masses of individuals in the population using the theoretical work found in the strength Pareto evolutionary algorithm. In the second stage (moving in space), how to handle the bat algorithm BAT to solve multiple functions is established. We applied the theoretical work of multi-objective particle swarm optimisation into the BAT algorithm to solve multiple functions. In the third stage, multi-objective GSA and multi-objective BAT are integrated to obtain the hybrid MOGSABAT algorithm. MOGSABAT is tested by adopting a three-part evaluation methodology that (1) describes the benchmarking of the optimisation problem (bi-objective and tri-objective) to evaluate the performance of the algorithm; (2) compares the performance of the algorithm with that of other intelligent computation techniques and parameter settings; and (3) evaluates the algorithm based on mean, standard deviation and Wilcoxon signed-rank test statistic of the function values. The optimisation results and discussion confirm that the MOGSABAT algorithm competes well with advanced metaheuristic algorithms and conventional methods. [ABSTRACT FROM AUTHOR]

Nature-inspired algorithms take inspiration from living things and imitate their behaviours to accomplish robust systems in engineering and computer science discipline. Symbiotic organisms search (SOS) algorithm is a recent metaheuristic algorithm inspired by symbiotic interaction between organisms in an ecosystem. Organisms develop symbiotic relationships such as mutualism, commensalism, and parasitism for their survival in ecosystem. SOS was introduced to solve continuous benchmark and engineering problems. The SOS has been shown to be robust and has faster convergence speed when compared with genetic algorithm, particle swarm optimization, differential evolution, and artificial bee colony which are the traditional metaheuristic algorithms. The interests of researchers in using SOS for handling optimization problems are increasing day by day, due to its successful application in solving optimization problems in science and engineering fields. Therefore, this paper presents a comprehensive survey of SOS advances and its applications, and this will be of benefit to the researchers engaged in the study of SOS algorithm. [ABSTRACT FROM AUTHOR]

Non-contiguous orthogonal frequency division multiplexing is considered as an auspicious scheme for the cognitive radio (CR) systems. It has the abilities such as spectral efficiency, multiple path delay spread and robustness against channel fading. However, due to its high sidelobes, it distorts the signals of the neighboring users including cognitive radio users (CRUs) as well as licensed users. In this paper, we proposed two novel metaheuristic algorithms, i.e., Cuckoo search algorithm and Firefly algorithm, to estimate the amplitudes of cancelation carriers which are used for the suppression of sidelobes. The effectiveness of the proposed algorithms is shown in single as well as the multiple CRUs environment. Simulations results in terms of power spectral density show that with the help of proposed algorithms significant reduction in sidelobes is achieved as compared with the existing methods. [ABSTRACT FROM AUTHOR]

Nature-inspired algorithms usually use some form of attraction and diffusion as a mechanism for exploitation and exploration. In this paper, we investigate the role of attraction and diffusion in algorithms and their ways in controlling the behavior and performance of nature-inspired algorithms. We highlight different ways of the implementations of attraction in algorithms such as the firefly algorithm, charged system search, and the gravitational search algorithm. We also analyze diffusion mechanisms such as random walks for exploration in algorithms. It is clear that attraction can be an effective way for enhancing exploitation, while diffusion is a common way for exploration. Furthermore, we also discuss the role of parameter tuning and parameter control in modern metaheuristic algorithms and then point out some key topics for further research. [ABSTRACT FROM AUTHOR]

In the present study, a new algorithm is developed for neural network training by combining a gradient-based and a meta-heuristic algorithm. The new algorithm benefits from simultaneous local and global search, eliminating the problem of getting stuck in local optimum. For this purpose, first the global search ability of the grey wolf optimizer (GWO) is improved with the Levy flight, a random walk in which the jump size follows the Levy distribution, which results in a more efficient global search in the search space thanks to the long jumps. Then, this improved algorithm is combined with back propagation (BP) to use the advantages of enhanced global search ability of GWO and local search ability of BP algorithm in training neural network. The performance of the proposed algorithm has been evaluated by comparing it against a number of well-known meta-heuristic algorithms using twelve classification and function-approximation datasets. [ABSTRACT FROM AUTHOR]

This paper develops an effective cuckoo search algorithm (ECSA) for searching optimal solutions for the problem of combined heat and power economic dispatch. The main task of the problem is to determine the optimal value of power of the pure power generators, of the heat of the pure heat generators and of both power and heat of cogenerators so that fuel cost is minimized while exactly meeting power and heat demands and power and heat limits as well as the complicated feasible operating zone of cogenerators. The proposed ECSA is a newly improved version of conventional cuckoo search algorithm to improve the quality of solutions and reduce the maximum number of iterations based on two modified techniques. The first technique is based on the ratio of the difference between the fitness function value of each solution and the lowest fitness function value of the best current solution to the lowest one to determine an effective operation for producing the second new solution generation. The second technique aims to integrate both previous and current solutions into one group and sort them in the descending order of fitness value. The effectiveness of ECSA has been validated via six cases corresponding to six test systems where the scale of the systems is ranged from the smallest system with four units to the largest one with forty-eight units with valve point loading effects. The comparisons of obtained results with other existing methods have indicated that the proposed ECSA is very effective and robust for finding optimal solutions for the CHPED problem. [ABSTRACT FROM AUTHOR]

One important aspect that should be achieved during the operation of the distribution network is minimizing the total active loss. This objective can be achieved by network reconfiguration in which switching events such as closing tie switches or opening sectionalizing switches are efficiently determined. This paper presents a reliable meta-heuristic algorithm for optimal reconfiguration of the distribution network which is binary particle swarm optimization gravity search algorithm (BPSOGSA). The methodology is applied on four test systems: 16-bus system, 33-bus system, 69-bus system and 119-bus system. Reliability indices, system average interruption frequency, system average interruption duration and energy not supplied, are incorporated to check the validity of the network after reconfiguration process. Comparison with other reported previous methods is performed; the power loss is reduced by 9.3242% in 16-bus system, for 33-bus system; the power loss is reduced by 31.46%. In case of 69-bus system, the power loss is reduced by 56.1761%, while for 119-bus, the power loss is reduced by 33.7216%. Additionally, the performance of the proposed BPSOGSA is the best one compared with the others for all studied cases. The obtained results prove the reliability of the proposed methodology. [ABSTRACT FROM AUTHOR]

In this work, the most common problem of the modern power system named optimal power flow (OPF) is optimized using the novel metaheuristic optimization algorithm Moth-Flame Optimizer (MFO). The MFO is inspired by the navigation strategy of moths in universe. MFO has a fast convergence rate due to the use of roulette wheel selection method. For the OPF solution, standard IEEE-30 bus test system is used. MFO is applied to solve the proposed problem. The problems considered in the OPF are fuel cost reduction, voltage profile improvement, voltage stability enhancement, active power loss minimization and reactive power loss minimization. The results obtained by MFO are compared with other techniques such as Flower Pollination Algorithm (FPA) and particle swarm optimizer (PSO). Results show that MFO gives better optimization values as compared with FPA and PSO which verifies the effectiveness of the suggested algorithm. [ABSTRACT FROM AUTHOR]

Nature-inspired metaheuristic algorithms are considered as the most effective techniques for solving various optimization problems. This paper provides a briefly review of the key features of the cuckoo-inspired metaheuristics: cuckoo search (CS) and cuckoo optimization algorithm (COA). In addition, it discusses some of their important and emerging studies, investigates their applications in several fields, and finally clarifies the differences between both algorithms so as to remove confusion between them. [ABSTRACT FROM AUTHOR]

This work investigates the performance of a hybrid population-based meta-heuristic with an external memory structure of a hybrid elitist-ant system (elitist-AS). This memory is known as an elite pool, which contains high quality and diverse solutions to maintain a balance between diversity and quality of the search. This may guarantee the effectiveness and efficiency of the search, which could enhance the performance of the algorithm across different instances. A very well known and intensively studied NP-hard optimization problem has been selected to test the performance of the hybrid elitist-AS via its consistency, effectiveness and efficiency. This famous problem is the symmetric traveling salesman problem. The elitist-AS is a class of ant colony optimization techniques which are known to be outstanding for the traveling salesman problem where they have the ability to find the shortest tours guided by the heuristic and the pheromone trail information. An iterated local search is combined with elitist-AS to intensify the search around elite solution and maintains the solution's exploitation mechanism. Experimental results showed that the performance, compared to the best known results, is optimal for many instances. This finding indicates the effectiveness, efficiency and consistency in diversifying the search while intensifying high-quality solutions. This outstanding performance is due to the utilization of an elite pool along with diversification and intensification mechanisms. In addition, this work proposes two instances that consist of 26 Jordanian cities and 1094 Jordanian locations which have been generated based on coordinates and distances similar to the format of the selected symmetric traveling salesman problem. This step is meant to contribute to finding a solution for a real-world problem and further test the performance of the hybrid elitist-AS. [ABSTRACT FROM AUTHOR]

Hill climbing method is an optimization technique that is able to build a search trajectory in the search space until reaching the local optima. It only accepts the uphill movement which leads it to easily get stuck in local optima. Several extensions to hill climbing have been proposed to overcome such problem such as Simulated Annealing, Tabu Search. In this paper, an extension version of hill climbing method has been proposed and called $$\beta$$ -hill climbing. A stochastic operator called $$\beta$$ -operator is utilized in hill climbing to control the balance between the exploration and exploitation during the search. The proposed method has been evaluated using IEEE-CEC2005 global optimization functions. The results show that the proposed method is a very efficient enhancement to the hill climbing providing powerful results when it compares with other advanced methods using the same global optimization functions. [ABSTRACT FROM AUTHOR]

Artificial neural networks (ANNs) are being used increasingly to forecast rainfall. In this study, several meta-heuristic algorithms are applied to train an ANN in order to improve the accuracy of rainfall forecasting. Centripetal accelerated particle swarm optimization (CAPSO), a gravitational search algorithm and an imperialist competitive algorithm train a multilayer perceptron (MLP) network as a feed-forward ANN for rainfall forecasting in Johor State, Malaysia. They are employed to forecast the average monthly rainfall in the next 5 and 10 years using the two modes of original (without data preprocessing) and data preprocessing with singular spectrum analysis. Additionally, for each month, the average monthly rainfall during the last 5 years is computed and a month with less rainfall than the average is classified as 0 (light rainfall month), otherwise as 1 (heavy rainfall month). The attributes used in the classification can be applied to forecast the monthly rainfall. The proposed methods integrate the accuracy and structure of ANN simultaneously. The result showed that the hybrid learning of MLP with the CAPSO algorithm provided higher rainfall forecasting accuracy, lower error and higher classification accuracy. One of the main advantages of CAPSO compared with the other algorithms to train MLP includes the following: The algorithm has no need to tune any algorithmic parameter and it shows good performance on unseen data (testing data). [ABSTRACT FROM AUTHOR]

This paper proposes a novel nature-inspired algorithm called Multi-Verse Optimizer (MVO). The main inspirations of this algorithm are based on three concepts in cosmology: white hole, black hole, and wormhole. The mathematical models of these three concepts are developed to perform exploration, exploitation, and local search, respectively. The MVO algorithm is first benchmarked on 19 challenging test problems. It is then applied to five real engineering problems to further confirm its performance. To validate the results, MVO is compared with four well-known algorithms: Grey Wolf Optimizer, Particle Swarm Optimization, Genetic Algorithm, and Gravitational Search Algorithm. The results prove that the proposed algorithm is able to provide very competitive results and outperforms the best algorithms in the literature on the majority of the test beds. The results of the real case studies also demonstrate the potential of MVO in solving real problems with unknown search spaces. Note that the source codes of the proposed MVO algorithm are publicly available at . [ABSTRACT FROM AUTHOR]

Optimization problems can become intractable when the search space undergoes tremendous growth. Heuristic optimization methods have therefore been created that can search the very large spaces of candidate solutions. These methods, also called metaheuristics, are the general skeletons of algorithms that can be modified and extended to suit a wide range of optimization problems. Various researchers have invented a collection of metaheuristics inspired by the movements of animals and insects (e.g., firefly, cuckoos, bats and accelerated PSO) with the advantages of efficient computation and easy implementation. This paper studies a relatively new bio-inspired heuristic optimization algorithm called the Wolf Search Algorithm (WSA) that imitates the way wolves search for food and survive by avoiding their enemies. The WSA is tested quantitatively with different values of parameters and compared to other metaheuristic algorithms under a range of popular non-convex functions used as performance test problems for optimization algorithms, with superior results observed in most tests. [ABSTRACT FROM AUTHOR]

Test data generation is always a key task in the field of software testing. In recent years, meta-heuristic search techniques have been considered as an effective way to assist test data generation in software structural testing. In this way, some representative test cases with high-coverage capability can be picked out from program input space. Harmony search (HS) is a recently developed algorithm and has been vigorously applied to various optimization problems. In the paper, we attempt to apply harmony search algorithm to generate test data satisfying branch coverage. At the preprocessing stage, the probes used for gathering coverage information are inserted into all branches via program static analysis. At the same time, the encoding and decoding styles between a test case and a harmony are also determined in advance. At the stage of test data searching, the subset of test data that has much stronger covering ability is stored in harmony memory. During the evolution process, one part of test suite is selected and adjusted from the harmony memory, and the other part is randomly generated from input space. Once a test suite is yielded after one-round search, its coverage can be measured by fitness function in our search algorithm. In our work, a new fitness function for branch coverage is constructed by comprehensively considering branch distance and branch weight. Here, the branch weight is determined by branch information in program, that is, the nesting level of a specific branch and the predicate types in it. Subsequently, the computed coverage metric is used for updating the test suite in the next round of searching. In order to validate the effectiveness of our proposed method, eight well-known programs are used for experimental evaluation. Experimental results show that the coverage of HS-based method is usually higher than those of other search algorithms, such as simulated annealing (SA) and genetic algorithm (GA). Meanwhile, HS demonstrates greater stability than SA and GA when varying the population size or performing repeated trials. That is to say, music-inspired HS algorithm is more suitable to generate test data for branch coverage in software structural testing. [ABSTRACT FROM AUTHOR]

In this paper, we present an improved and discrete version of the Cuckoo Search (CS) algorithm to solve the famous traveling salesman problem (TSP), an NP-hard combinatorial optimisation problem. CS is a metaheuristic search algorithm which was recently developed by Xin-She Yang and Suash Deb in 2009, inspired by the breeding behaviour of cuckoos. This new algorithm has proved to be very effective in solving continuous optimisation problems. We now extend and improve CS by reconstructing its population and introducing a new category of cuckoos so that it can solve combinatorial problems as well as continuous problems. The performance of the proposed discrete cuckoo search (DCS) is tested against a set of benchmarks of symmetric TSP from the well-known TSPLIB library. The results of the tests show that DCS is superior to some other metaheuristics. [ABSTRACT FROM AUTHOR]