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• A feature selection method based on binary dandelion algorithm is proposed. • The algorithm applies a sine-cosine operator and a restart strategy. • Mutual information and quick bit mutation improve the performance of the algorithm. • The algorithm performs well on datasets with larger dimensions. Feature selection (FS) is an important data preprocessing technology for machine learning and data mining. Metaheuristic algorithm (MH) has been widely used in feature selection because of its powerful search function. This paper presents an improved Binary Dandelion Algorithm using Sine Cosine operator and Restart strategy (SCRBDA) for feature selection. First, the sine cosine operator is used in the radius formula of the core dandelions (CD), which significantly enhances the ability of algorithm development and exploration. Secondly, the algorithm uses a restart strategy to increase its ability to get rid of local optimum. Thirdly, mutual information is used to guide the generation of some dandelions, which pays more attention to the correlation between the selected features and categories. Finally, quick bit mutation is used as the mutation strategy to improve the diversity of the population. The SCRBDA proposed in this paper was tested on 18 datasets of different sizes from UCI machine learning database. The SCRBDA was compared with 8 other classical feature selection algorithms, and the performance of the proposed algorithm was evaluated through feature subset size, classification accuracy, fitness value, and F1-score. The experimental results show that SCRBDA achieves the best performance, which has stronger feature reduction ability and achieves better overall performance on most datasets. Especially on large-scale datasets, SCRBDA can obtain extremely smaller feature subsets while maintaining much higher classification accuracy, and satisfactory F1-score. [ABSTRACT FROM AUTHOR]

• Moisture affects microbial succession and volatiles generation by mediated with Lactobacillus. • The ratio of ethyl acetate to ethyl lactate and content of acid, alcohol positively relate to moisture. • Increase moisture accelerate formation of Lactobacillus -dominated fermentation microbiota. • Increase moisture enhance complexity and stability of baijiu fermentation ecological networks. Today, image segmentation methods are widely used for various applications, including object detection. Multilevel Thresholding Image Segmentation (MTIS) methods are among the efficient methods for image segmentation. In MTIS methods, it is very important to find the thresholds that gives the best performance for the MTIS and better separate and detect the objects on the image from the image background. Meta-Heuristic (MH) algorithms are among the strategies that can achieve good results in obtaining optimal thresholds to solve this problem. In this paper, we use the combination of Arithmetic Optimization Algorithm (AOA) and Harris Hawks Optimizer (HHO) to improve AOA in exploitation phase, and achieve an optimal threshold vector for MTIS. Therefore, our new hybrid AOA-HHO algorithm solves the MTIS problem with better quality than both AOA and HHO algorithms and some other MH algorithms, and can obtain better thresholds that increase the performance of the MTIS system than AOA and HHO. AOA is powerful in the exploration, and HHO in exploitation phase is powerful. Therefore, AOA-HHO uses the features of both algorithms to search the entire search space locally and globally to find the best find the solution, the high power of the AOA exploration phase, and the high power of the HHO exploitation phase. Also, we use a mathematical equation as the fitness function, that is obtained by using image features. A series of experiments were performed using seven different threshold levels on the test images. Experiments show that AOA-HHO method is better than the compared algorithms and even HHO and AOA in terms of image segmentation accuracy, fitness function value, Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and execution time. [ABSTRACT FROM AUTHOR]

The motivation of this paper follows strictly from industrial needs to improve competitiveness in railway companies by the efficient rolling stock utilization affected by the obligatory preventive maintenances of vehicles, which cause their temporary unavailability. Nevertheless, it is economically essential to minimize the total time on maintenances of vehicles, which can be achieved in a certain range by the scheduling of transport tasks taking into account maintenance policies of the vehicles, their mileage and date since the last maintenance, and the workload related to the tasks. Therefore, to face the problem, we model the consider issue as the job scheduling problem to maximize the availability of railway vehicles under preventive maintenances. We prove that the problem is strongly NP-hard and we propose efficient heuristic and metaheuristic algorithms that are characterized by a low computational complexity and they are able to find solutions satisfying from the perspective of industrial practice. The simulations based on real-life data reveal that our approach can provide schedules, which significantly improve availability of locomotives resulting in high potential financial benefits for railway companies. • The total time of maintenance can be minimized by the scheduling of transport tasks • The railway vehicle availability problem under preventive maintenance is formulated • The considered problem is proved to be strongly NP-hard • The proposed scheduling algorithms improve availability of railway vehicles • The results obtained on real-life data are satisfying for the industrial practice. [ABSTRACT FROM AUTHOR]

Artificial bee colony (ABC) is a prominent algorithm that offers great exploration capabilities among various meta-heuristic algorithms. However, its monotonous and one-dimensional search strategy limits its searching performance in the solving process. Thus, to address this issue, a Q-learning based multi-strategy integrated ABC algorithm (QMABC) is proposed. In the QMABC, multiple search strategies are proposed to utilize different individual experiences and search approaches for solution updates. Then, Q-learning is employed for strategy selection. In comparison to previous studies, this paper introduces more effective state and action configurations within the framework of Q-learning. To evaluate the performance of the QMABC, CEC 2017 benchmark functions are adopted to compare it to different meta-heuristic algorithms including ABC based and non-ABC based algorithms. Moreover, applications in path planning are implemented to further verify the effectiveness of the QMABC. Overall, it should be highlighted that the proposed QMABC demonstrates superiority in both numerical and practical experiments. • A Q-learning based multi-strategy integrated ABC algorithm is proposed. • Multiple strategies are introduced to the onlooker bee phase. • Q-learning is utilized to adaptively determine the most suitable strategy. • Novel state and action settings are designed in Q-learning framework. • Proposed algorithm shows superiority in numerical and practical experiments. [ABSTRACT FROM AUTHOR]

One driving application for multi-robots is source seeking, especially in the hazardous environment. It consists of two essential subtasks: source location and path search. Nature inspired meta-heuristic is preferable in addressing the source location subtask which is an inversion problem, while the Astar algorithm and its variants are widely used for the path search subtask. In this paper, we present a multi-robot team simulator as a container which contains both algorithms as components. The simulator takes the constraints into consideration, including the size and the speed bound of each robot, the obstacle and collision avoidance. We provide a python implementation and example problems for research and test purposes. The well-structured code with object-oriented design can be conveniently upgraded by adding new excellent nature inspired metaheuristics, or extended to other source seeking problems in various field applications. The python code can be downloaded from the website: https://github.com/buctlab/source-seeking-multi-robot-team-simulator. • We developed a source-seeking multi-robot simulator containing many metaheuristics. • We implemented the multimodal version of metaheuristics in the simulator. • The simulator closely combines the metaheuristics with the Astar algorithm. [ABSTRACT FROM AUTHOR]

In this paper, a metaheuristic algorithm called PID-based search algorithm (PSA) is proposed for global optimization. The algorithm is based on an incremental PID algorithm that converges the entire population to an optimal state by continuously adjusting the system deviations. PSA is mathematically modeled and implemented to achieve optimization in a wide range of search spaces. PSA is used to solve CEC2017 benchmark test functions and six constrained problems. The optimization performance of PSA is verified by comparing it with seven metaheuristics proposed in recent years. The Kruskal-Wallis, Holm and Friedman tests verified the superiority of PSA in terms of statistical significance. The results show that PSA can be better balanced exploration and exploitation with strong optimization capability. Source codes of PSA are publicly available at https://ww2.mathworks.cn/matlabcentral/fileexchange/131534-pid-based-search-algorithm. [ABSTRACT FROM AUTHOR]

The last few years have seen an increasing demand for high-capacity Internet services, and this need has intensified in the years 2020 and 2021. In 2020 and 2021, Internet usage grew by 50% in several European countries, mainly due to home office, video streaming, hybrid teaching, and others. High-capacity optical networks usually meet this growing demand for Internet services. Thus, investigations that can improve the quality of optical networks are highly relevant in the current context. One of the research problems in this area is related to the physical topology design (PTD) of optical networks, which is classified as NP-hard. Several studies on PTD consider the application of meta-heuristics that obtain suboptimal solutions in a time compatible with engineering applications. However, meta-heuristics and local search techniques have been combined in several other optimization problems, which is not typical for the PTD problem. This paper proposes a solution to the PTD problem that combines a known multipurpose optimization algorithm, the NSGA-III, with operators considering the domain-specific knowledge of the problem to provide superior-quality networks. According to our results, the new proposal presents quality up to 8% higher than previous proposals concerning the hypervolume metrics (HV), maintaining a similar computational cost. • Proposal of pivot rules for local search in many objective algorithms for PTD. • Provide a method for efficient exploration of neighborhood for network optimization. • Proposal of a new local search that considers both convergence and diversity. • Hybridization of NSGA-III with the proposed LS, providing superior quality solutions. [ABSTRACT FROM AUTHOR]

Portfolio optimization is an essential issue in quantitative investing, which aims to find the best set of portfolios by allocating the proportion of assets. One of the most widely studied portfolio optimization models is the cardinality constrained mean–variance model, which incorporates real-world constraints on the number of selected assets and lower and upper bounds on the proportion of each asset. This paper presents a novel metaheuristic algorithm based on the Mayfly algorithm to solve the cardinality constrained mean–variance portfolio optimization problem. To better adapt to this problem, we design and introduce some new features to the proposed algorithm, including (1) a new cardinality constraint handling strategy; (2) a new local search strategy; and (3) changes to the crossover operator. We have designed comparison experiments for the proposed metaheuristic and evaluated its performance using five commonly used performance metrics. The experimental results show that the proposed approach achieves competitive performance on datasets of different sizes. The results also demonstrate the feasibility of this approach in solving the cardinality constrained mean–variance portfolio optimization problem. • The proposed algorithm is used for Cardinality Constrained Portfolio Optimization. • A fast cardinality constraint handling strategy. • A nuptial dance and bi-direction-based local search strategy. • Excellent comparison experimental results on benchmark from OR-Lib. [ABSTRACT FROM AUTHOR]

When planning maintenance tasks involving the switching off of parts of the network, electricity transmission operators must evaluate and take into account the associated risks. The French operator, RTE, has developed a three-step approach in which they first calculate the risks in every possible scenario, then plan the maintenance tasks by optimizing some risk measures, and finally validate the plan. In this paper, we have addressed the second step, developing mathematical models that approximate the original problem as well as a matheuristic algorithm. It is composed of three phases, combining integer linear models in the first phase with metaheuristics such as Variable Neighbourhood Descent and Path Relinking in the second and third phases. The computational results obtained on the benchmarks provided by the company show that our algorithm improves on the best reported solutions for most of the test instances. • We have developed a matheuristic algorithm for a real maintenance planning problem. • The algorithm combines integer linear models with GRASP, VND, and Path Relinking. • The solutions are refined in a final intensification phase. • Our algorithm improves on the best reported solutions for most of the instances. [ABSTRACT FROM AUTHOR]

In this paper we solve a practical variant of an arc routing problem. We target the close enough model in which clients can be served from relatively close arcs. This variant, known as the profitable close-enough arc routing problem, models real situations, such as inventory management or automated meter reading. We propose a heuristic based on the variable neighborhood search methodology to maximize the sum of profits of the clients served (penalized with the distance traveled). Our method incorporates efficient search strategies to speed up the optimization process, as required in practical applications. We present extensive experimentation over a benchmark of previously reported instances. Specifically, we first set the key search parameters of our method, and then compare it with the state-of-the-art heuristics for this problem. Our heuristic outperforms the previous algorithms published for this problem, as confirmed by the statistical analysis, which permits to draw significant conclusions. • A literature overview on close-enough is carried out. • A MultiStart-VNS algorithm is proposed and adapted to solve the PCEARP. • The experimental results proved the efficiency of the proposed algorithm. • The metaheuristic algorithm outperforms other existing methods for the problem. • The method has a great potential to be applied in emerging real-life problems. [ABSTRACT FROM AUTHOR]

In this study, an energy efficiency study has been carried out through the moment values of a quadrotor applied in the position control with the differential evolution algorithm (DE), which is under the evolutionary algorithms among meta-heuristic algorithms particle swarm optimization (PSO) which is among swarm-based algorithms and gravity search algorithm (GSA) among physics-based algorithms and charged system search (CSS) algorithm, which are all well-known in the literature. The efficient use of energy in electrically powered flying mechanisms is important in terms of completing its task. The aim of this study is to obtain proportional–integral–derivative (PID) coefficients that minimize the energy consumption caused by quadrotor moments. First of all, 12 PID coefficients calculated with traditional Ziegler Nicholes, and trying-error methods for the control of the quadrotor have been trained with DE, PSO, GSA and CSS algorithms and their cost values have been found for this problem. In the study, optimum PID coefficients have been found separately with each algorithm and torque data giving energy efficiency have been obtained by applying them to the state equations. Although the cost minimization convergence value obtained from DE does not look good compared to other algorithms, it has been observed that the moment values of the obtained PID coefficients in the control program are lower. In the study, it has been shown that the PID coefficients obtained from DE reach the target with approximately less energy than the maximum-moment value of the PSO, which provides the closest moment value. It can be thought that this value contributes to energy consumption in each orbital movement of the quadrotor. The simulation results show that the coefficients obtained from DE provide better energy efficiency and are superior to other algorithms compared in terms of energy efficiency. • PID coefficients for quadrotor control are optimized with DE,PSO,CSS,GSA algorithms • The torque values to be applied to each motor were adjusted with these coefficients • The total force applied with DEPID in the z direction is smaller than others • DEPID consumes less energy than the other algorithms in the paper [ABSTRACT FROM AUTHOR]

In this study, an energy efficiency study has been carried out through the moment values of a quadrotor applied in the position control with the differential evolution algorithm (DE), which is under the evolutionary algorithms among meta-heuristic algorithms particle swarm optimization (PSO) which is among swarm-based algorithms and gravity search algorithm (GSA) among physics-based algorithms and charged system search (CSS) algorithm, which are all well-known in the literature. The efficient use of energy in electrically powered flying mechanisms is important in terms of completing its task. The aim of this study is to obtain proportional–integral–derivative (PID) coefficients that minimize the energy consumption caused by quadrotor moments. First of all, 12 PID coefficients calculated with traditional Ziegler Nicholes, and trying-error methods for the control of the quadrotor have been trained with DE, PSO, GSA and CSS algorithms and their cost values have been found for this problem. In the study, optimum PID coefficients have been found separately with each algorithm and torque data giving energy efficiency have been obtained by applying them to the state equations. Although the cost minimization convergence value obtained from DE does not look good compared to other algorithms, it has been observed that the moment values of the obtained PID coefficients in the control program are lower. In the study, it has been shown that the PID coefficients obtained from DE reach the target with approximately less energy than the maximum-moment value of the PSO, which provides the closest moment value. It can be thought that this value contributes to energy consumption in each orbital movement of the quadrotor. The simulation results show that the coefficients obtained from DE provide better energy efficiency and are superior to other algorithms compared in terms of energy efficiency. • PID coefficients for quadrotor control are optimized with DE,PSO,CSS,GSA algorithms • The torque values to be applied to each motor were adjusted with these coefficients • The total force applied with DEPID in the z direction is smaller than others • DEPID consumes less energy than the other algorithms in the paper [ABSTRACT FROM AUTHOR]

• This paper proposes an enhanced SCA algorithm variant, called ARSCA. • ARSCA improves the convergence accuracy and avoids premature convergence. • This paper compares ARSCA with classical algorithms, variations of other algorithms. • ARSCA is applied to six engineering design problems and its results are satisfactory. The sine cosine algorithm (SCA) is a well-known meta-heuristic optimization algorithm. SCA has received much attention in various optimization fields due to its simple structure and excellent optimization capabilities. However, the dimension of objective function also increases with the increasing complexity of optimization tasks. This makes the original SCA appear to have insufficient optimization capability and likely to fall into premature convergence. A multi-mechanism acting variant of SCA, called ARSCA, is proposed to address the above deficiencies. ARSCA is an enhanced SCA algorithm based on the adaptive quadratic interpolation mechanism (AQIM) and Rounding mechanism (RM). RM enables a more balanced state between exploration and exploitation of the ARSCA. AQIM enhances local exploitation capabilities. To verify the performance of ARSCA, we compared ARSCA with some advanced traditional optimization algorithms and variants of algorithms for 30 consecutive benchmark functions of IEEE CEC2014. In addition, ARSCA was applied to 6 constrained engineering optimization problems. These six algorithms include the tension–compression spring design problem, the welded beam design problem, the pressure vessel design problem, the I-beam design problem, the speed reducer design problem, and the three-bar design problem. Experimental results show that ARSCA outperforms its competitors in both the solution quality and the ability to jump out of the local optimum. The relevant codes for the paper are publicly available at https://github.com/YangXiao9799/paper_ARSCA. [ABSTRACT FROM AUTHOR]

• This paper proposes an enhanced SCA algorithm variant, called ARSCA. • ARSCA improves the convergence accuracy and avoids premature convergence. • This paper compares ARSCA with classical algorithms, variations of other algorithms. • ARSCA is applied to six engineering design problems and its results are satisfactory. The sine cosine algorithm (SCA) is a well-known meta-heuristic optimization algorithm. SCA has received much attention in various optimization fields due to its simple structure and excellent optimization capabilities. However, the dimension of objective function also increases with the increasing complexity of optimization tasks. This makes the original SCA appear to have insufficient optimization capability and likely to fall into premature convergence. A multi-mechanism acting variant of SCA, called ARSCA, is proposed to address the above deficiencies. ARSCA is an enhanced SCA algorithm based on the adaptive quadratic interpolation mechanism (AQIM) and Rounding mechanism (RM). RM enables a more balanced state between exploration and exploitation of the ARSCA. AQIM enhances local exploitation capabilities. To verify the performance of ARSCA, we compared ARSCA with some advanced traditional optimization algorithms and variants of algorithms for 30 consecutive benchmark functions of IEEE CEC2014. In addition, ARSCA was applied to 6 constrained engineering optimization problems. These six algorithms include the tension–compression spring design problem, the welded beam design problem, the pressure vessel design problem, the I-beam design problem, the speed reducer design problem, and the three-bar design problem. Experimental results show that ARSCA outperforms its competitors in both the solution quality and the ability to jump out of the local optimum. The relevant codes for the paper are publicly available at https://github.com/YangXiao9799/paper_ARSCA. [ABSTRACT FROM AUTHOR]

A large number of metaheuristic algorithms have been proposed in the last three decades, but no metaheuristic algorithm is superior to the others for all the optimization problems. It is laborious to select an appropriate metaheuristic algorithm to solve a problem, especially for laymen. In this paper, a framework for co-evolutionary algorithm using Q-learning with the meme is proposed, which is called as QLMA. The solution generation method in the metaheuristic algorithm is named meme, which is also viewed as the action for an agent in Q-learning, and multiple memes form the action set. In the initial stage, the tent map and opposition based learning are employed to obtain the initial population. In updating stage, a new population is generated by an action that is chosen from the action set by the agent in Q-learning, then the disruption operation is applied, avoiding excessive aggregation of solutions around the current global optimal solution and improving the balance between exploration and exploitation. QLMA is compared to sixteen algorithms on 23 classical benchmark functions, CEC 2017, and CEC 2019 benchmark functions. The experimental results demonstrate that QLMA is superior to the peer algorithms and has a good balance between exploration and exploitation. [ABSTRACT FROM AUTHOR]

Metaheuristics, providing high level guidelines for heuristic optimisation, have successfully been applied to many complex problems over the past decades. However, their performances often vary depending on the choice of the initial settings for their parameters and operators along with the characteristics of the given problem instance handled. Hence, there is a growing interest into designing adaptive search methods that automate the selection of efficient operators and setting of their parameters during the search process. In this study, an adaptive binary parallel evolutionary algorithm, referred to as ABPEA, is introduced for solving the uncapacitated facility location problem which is proven to be an NP-hard optimisation problem. The approach uses a unary and two other binary operators. A reinforcement learning mechanism is used for assigning credits to operators considering their recent impact on generating improved solutions to the problem instance in hand. An operator is selected adaptively with a greedy policy for perturbing a solution. The performance of the proposed approach is evaluated on a set of well-known benchmark instances using ORLib and M*, and its scaling capacity by running it with different starting points on an increasing number of threads. Parameters are adjusted to derive the best configuration of three different rewarding schemes, which are instant, average and extreme. A performance comparison to the other state-of-the-art algorithms illustrates the superiority of ABPEA. Moreover, ABPEA provides up to a factor of 3.9 times acceleration when compared to the sequential algorithm based on a single-operator. • This paper proposes a multi-threaded adaptive evolutionary algorithm. • The proposed approach (ABPEA) is tested on the well-known binary-encoded problem UFLP. • An operator is selected adaptively with a greedy policy for perturbing a solution. • Three rewarding schemes, which are instant, average and extreme, are discussed. • Effect of varying the number of threads on the performance of ABPEA is investigated. [ABSTRACT FROM AUTHOR]

The Asymmetric Traveling Salesman Problem (ATSP), as a branch of the traveling salesman problem, aims to find a group of circuits when a salesman travels all cities at the minimum cost and returns to the starting city under the condition of different round-trip costs between the two cities. Mayfly Algorithm (MA) is a swarm-based metaheuristic algorithm, which has been applied to many fields successfully. In this paper, we arrange all cities on the sphere. A Discrete Mayfly Algorithm (DMA) for solving spherical asymmetric traveling salesman problem is proposed. The algorithm applies inver-over operator, crossover operator and 3-opt operator to simplify the parameters of the algorithm, enrich the diversity of the population and enhance the local search ability. By experimenting, analyzing and comparing the effects of DMA and other metaheuristic algorithms in twelve scales, this study proves that the proposed algorithm is superior to others for the spherical ATSP, especially in high-dimensional cases. [ABSTRACT FROM AUTHOR]

Wind resource is one of the most promising renewable energy, which has become a suitable replacement for fossil fuels. Optimizing the transferring wind energy from a wind turbine is essential to obtain the maximum power output as other variables are uncontrollable. This paper presents four different optimization algorithms, namely ant lion optimization (ALO), whale optimization algorithm (WOA), particle swarm optimization (PSO), and crow search optimization (CSO), considering a hybrid decision-making model to compare the performances of wind energy optimization. In the first phase, the evolutionary algorithms are defined based on several factors to meet the need for wind energy based on volumetric and time reliability, reversibility, and vulnerability as well as evaluate optimized energy to the subscriber from the Gansu region. In the second phase, the ordinal priority approach (OPA) is coupled with VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method to rank the evolutionary algorithms. Then, the results are compared with the absolute optimal response based on the nonlinear programming method obtained from GAMS software. The results demonstrate that an ALO outperforms other algorithms. The average accuracy of ALO is 92%. CSO is the least accurate with 55% of the absolute optimal response. ALO is found to be faster, more efficient, and achieved economy and reliability as compared to other optimization algorithms for solving the problem under consideration. It is shown that the applied models are robust, effective, and able to save costs. [ABSTRACT FROM AUTHOR]

We introduce a multidimensional multiblock clustering (MDMBC) algorithm in this paper. MDMBC can generate overlapping clusters with similar values along clusters of dimensions. The parsimonious binary vector representation of multidimensional clusters lends itself to the application of efficient meta-heuristic optimization algorithms. In this paper, a hill-climbing (HC) greedy search algorithm has been presented that can be extended by several stochastic and population-based meta-heuristic frameworks. The benefits of the algorithm are demonstrated in a bi-clustering benchmark problem and in the analysis of the Leiden higher education ranking system, which measures the scientific performance of 903 institutions along four dimensions of 20 indicators representing publication output and collaboration in different scientific fields and time periods. • Multiblock clustering algorithm generates overlapping clusters along clusters of dimensions. • Generalized parsimonious binary vector-based representation for higher-dimensions. • The proposed algorithms are demonstrated via an open multidimensional dataset. [ABSTRACT FROM AUTHOR]

Many engineering and scientific problems in the real-world boil down to optimization problems, which are difficult to solve by using traditional methods. Meta-heuristics are appealing algorithms for solving optimization problems while keeping computational costs reasonable. The marine predators algorithm (MPA) is a modern optimization meta-heuristic, inspired by widespread Lévy and Brownian foraging strategies in ocean predators as well as optimal encounter rate strategies in biological interactions between predator and prey. However, MPA is not without its shortcomings. In this paper, a quasi-opposition based learning and Q-learning based marine predators algorithm (QQLMPA) is proposed. This offers multiple improvements over standard MPA. Primely, Q-learning allows MPA to fully use the information generated by previous iterations. And also, quasi-opposition based learning serves to increase population diversity, reducing the risk of convergence to inferior local optima. Numerical experiments demonstrate better performance by QQLMPA on 32 benchmark optimization functions and three engineering problems: designs of pressure vessel, hydro-static thrust bearing, and speed reducer. • Q-learning is introduced to MPA for adaptively choosing search mode. • Quasi-opposition learning is used to enhance the exploration of MPA. • The proposed optimizer can achieve great performance for continuous optimization. [ABSTRACT FROM AUTHOR]

• The paper benefits from Reinforcement Learning in industrial control applications. • TD3 policy gradient is implemented to learn optimal PI controller dynamics. • The actor-network learns the optimal PI dynamics using the reward mechanism. • The performance of the optimal PI dynamics is compared with the other results. To benefit from the advantages of Reinforcement Learning (RL) in industrial control applications, RL methods can be used for optimal tuning of the classical controllers based on the simulation scenarios of operating conditions. In this study, the Twin Delay Deep Deterministic (TD3) policy gradient method, which is an effective actor-critic RL strategy, is implemented to learn optimal Proportional Integral (PI) controller dynamics from a Direct Current (DC) motor speed control simulation environment. For this purpose, the PI controller dynamics are introduced to the actor-network by using the PI-based observer states from the control simulation environment. A suitable Simulink simulation environment is adapted to perform the training process of the TD3 algorithm. The actor-network learns the optimal PI controller dynamics by using the reward mechanism that implements the minimization of the optimal control objective function. A setpoint filter is used to describe the desired setpoint response, and step disturbance signals with random amplitude are incorporated in the simulation environment to improve disturbance rejection control skills with the help of experience based learning in the designed control simulation environment. When the training task is completed, the optimal PI controller coefficients are obtained from the weight coefficients of the actor-network. The performance of the optimal PI dynamics, which were learned by using the TD3 algorithm and Deep Deterministic Policy Gradient algorithm, are compared. Moreover, control performance improvement of this RL based PI controller tuning method (RL-PI) is demonstrated relative to performances of both integer and fractional order PI controllers that were tuned by using several popular metaheuristic optimization algorithms such as Genetic Algorithm, Particle Swarm Optimization, Grey Wolf Optimization and Differential Evolution. [ABSTRACT FROM AUTHOR]

• The path planning and path smoothing are combined through the high-order continuous Bezier curve. • An improved QPSO that incorporates two search mechanisms from bald eagle search (BES) is proposed. • The concept of semi-randomization is infused in some of our modifications in BES-MQPSO. • Enhanced intensive exploitation is performed by incorporating BES search and swoop mechanisms. • It may be the first time to apply BES tricks to help to enhance QPSO and into the path planning field. The design of path planning algorithms to obtain a smooth path that satisfies constraints such as obstacle avoidance, minimized cost, and dynamic feasibility for mobile robots in the workspace is a hot issue in the field of intelligent robotics. The metaheuristic optimization algorithm, which aims to solve path planning problems, is more efficient compared to traditional algorithms. In the current metaheuristic approaches, avoiding local minimum traps is the core goal of the scheme. In this paper, with the benefit of a high-order continuous Bezier curve, we construct an optimization problem of smooth path planning based on the length and the requirement of collision-free safety as constraints of the robot's expected path. Then, the smooth path planning problem is transformed into an optimization problem that searches the locations of control nodes of the Bezier curve. We solve the constructed path planning problem with our modified quantum particle swarm optimization algorithm (QPSO). Specifically, to overcome the shortcomings of the current PSO and QPSO-based particle swarm algorithms like prematurity, unbalanced global search, local exploitation, and local minima, we implement an improved QPSO algorithm denoted as BES-MQPSO that incorporates the search and swoop mechanisms from bald eagle search (BES) strategies. The method consists of four improved modifications: an improved local attractor, improved feature length, BES-enhanced intensive exploitation Strategy, and random variation mechanism. Our proposed BES-MQPSO with these four modifications is tested with benchmark functions and then applied to the smooth path planning problem. Finally, the superiority of BES-MQPSO is confirmed, and the ideal smooth path of the mobile robot is successfully planned. The planned path length is reduced by 4.45% and 2.82% compared to e-QPSO and SDEQPSO respectively, while the standard deviation is reduced by 67.46% and 69.82%, respectively. [ABSTRACT FROM AUTHOR]

• A generalized decoupling metaheuristic RBDO framework is proposed. • A particle's memory saving strategy is developed to provide effective guidance. • A new adaptive fractional-order equilibrium optimizer algorithm is developed. • Numerical and engineering examples illustrate the superiority of the proposed algorithm. Reliability-based design optimization (RBDO) algorithm is to minimize the objective under the probabilistic factors. While gradient-based and classical evolutionary RBDO algorithms provide promising performance on simple optimization problems, they are likely to perform poorly on challenging problems, including the multimodal functions, discrete design spaces, non-differential problems, etc. This paper proposes a unified framework to improve the performance of existing RBDO algorithms for complex RBDO problems. Our framework is based on three new strategies: generalized decoupling evolutionary and metaheuristic RBDO framework, particle's memory saving strategy, and adaptive fractional-order equilibrium optimizer algorithm. The proposed algorithm is characterized by a decoupling strategy to enable the parallel operation of the inner reliability computation and outer deterministic optimization, a particle's memory saving strategy to provide effective guidance from the previous iteration, and the adaptive fractional-order equilibrium optimizer algorithm to enhance the search efficiency and global convergence capacity. To evaluate the performance of the proposed algorithm, a wide range of experiments are conducted on different types of use cases. The experimental results demonstrate that our algorithm provides superior performance over other comparative algorithms. [ABSTRACT FROM AUTHOR]

Sine cosine algorithm (SCA) is a recently developed and widely used metaheuristic to perform global optimization tasks. Due to its simplicity in structure and reasonable performance, it has been utilized to solve several real-world applications. This paper proposes an alternate version of the SCA by adopting the greedy approach of search, crossover and exponentially decreased transition control parameter to overcome the issues of low exploitation, insufficient diversity and premature convergence. The proposed algorithm, called ECr-SCA, is validated and compared with the original SCA using computational time, diversity, performance index, statistical and convergence analysis on a set of 23 standard benchmark problems. Later, the proposed ECr-SCA is compared with seventeen other algorithms including improved versions of the SCA and state-of-the-art algorithms. Furthermore, the ECr-SCA is used to train multi-layer perceptron and the results are compared with variants of SCA and other metaheuristics. Overall comparison based on several different metrics illustrates the significant improvement in the search strategy of the SCA by the proposal of the ECr-SCA. • The enhanced sine cosine algorithm (ECr-SCA) is proposed for global optimization. • A greedy search and modified transition parameter are used to increase exploitation. • The multi-parent crossover is embedded to enhance diversity and global search. • The excellent performance of the ECr-SCA is validated on benchmark problems. • The ECr-SCA is also used to train multilayer perceptron. [ABSTRACT FROM AUTHOR]

• GBO-kNN is a proposed framework based on a wrapper approach for features selection. • The aim of the research is to reach maximum classification accuracy. • The performance of GBO-kNN is evaluated against real benchmark datasets. • The GBO-kNN performance is compared against seven recent metaheuristic algorithms. • Results showed high effectiveness on one dataset and moderate on another. Virtual Screening (VS) in drug discovery campaigns deals with enormously large databases containing massive redundant and/or irrelevant features. Thus, the pre-selection process of virtual screening requires developing a fast and precise classification methodology for filtering such a huge database. This paper proposed a framework depending on a wrapper selection approach for features selection. It consists of 1) an optimizer: Gradient-Based Optimizer (GBO), that hybridized with 2) the classifier: k-nearest neighbor (k-NN). The performance of the introduced framework, GBO-kNN, is estimated using real-world benchmark datasets; the QSAR Biodegradation which is composed of 41 features, and the Monoamine Oxidase (MAO) that consisted of 1665 features. the GBO-kNN framework is compared against seven recent swarm intelligence algorithms: Hybrid Harris Hawks Optimization Algorithm (HHO), Grey Wolf Optimization Algorithm (GWO), Butterfly Optimization Algorithm (BOA), Dragonfly Algorithm (DA), Moth-Flame Optimization Algorithm (MFO), Sine Cosine Algorithm (SCA), and Salp Swarm Algorithm (SSA) for reaching maximum classification accuracy. The experimental results exhibited that the proposed process, in comparison with other algorithms, achieved high effective accuracy of 98.8% over the high dimensional dataset (MAO) and a moderate effective performance over the low dimensional dataset (QSAR Biodegradation). The proposed framework source code is available at https://codeocean.com/capsule/9906421/tree/v1. [ABSTRACT FROM AUTHOR]

In the crowded environment of bio-inspired population-based metaheuristics, the Salp Swarm Optimization (SSO) algorithm recently appeared and immediately gained a lot of momentum. Inspired by the peculiar spatial arrangement of salp colonies, which are displaced in long chains following a leader, this algorithm seems to provide an interesting optimization performance. However, the original work was characterized by some conceptual and mathematical flaws, which influenced all ensuing papers on the subject. In this manuscript, we perform a critical review of SSO, highlighting all the issues present in the literature and their negative effects on the optimization process carried out by this algorithm. We also propose a mathematically correct version of SSO, named Amended Salp Swarm Optimizer (ASSO) that fixes all the discussed problems. We benchmarked the performance of ASSO on a set of tailored experiments, showing that it is able to achieve better results than the original SSO. Finally, we performed an extensive study aimed at understanding whether SSO and its variants provide advantages compared to other metaheuristics. The experimental results, where SSO cannot outperform simple well-known metaheuristics, suggest that the scientific community can safely abandon SSO. • Critical analysis of the widely-used salp optimization algorithm (SSO). • Definition of an amended version of SSO that fixed SSO's structural bias. • Extensive experimental phase using the CEC 2017 benchmark suite. • Results show that SSO does not provide any advantage w.r.t. existing heuristics. • Metaphor-based approach to design metaheuristics may introduce bias. [ABSTRACT FROM AUTHOR]

• Enhance EO algorithm using ReliefF algorithm and the local search strategy. • Propose new feature selection method based on a hybridization RBEO-LS method. • Evaluate RBEO-LS using 16 UCI datasets and 10 high dimensional biological datasets. • Results show superiority of RBEO-LS among other state-of-the-art methods. Feature selection (FS) is an important task in any classification process and aims to choose the smallest features number that yields higher classification accuracy. FS can be formulated as a combinatorial NP-hard problem for which robust metaheuristics are used as efficient wrapper-based FS approaches. However, when applied for high dimensional datasets that present large features number and few samples, the effectiveness of such wrapper-metaheuristics degraded, and their computation costs increased. To tackle this problem, we propose in this paper a hybrid FS approach based on the ReliefF filter method and a novel metaheuristic Equilibrium Optimizer (EO). The proposed method, called RBEO-LS, is composed of two phases. In the first phase, the ReliefF algorithm is used as a preprocessing step to assign weights for features, which estimate their relevance to the classification task. In the second phase, the binary EO (BEO) is used as a wrapper search approach. The features are ranked according to their weights and are used for the initialization of the BEO population. We embedded the BEO with a local search strategy to improve its performance by adding relevant features and removing redundant ones from the features subset guided by the features ranking and the Pearson coefficient correlation. The performance of the developed algorithm has been evaluated on sixteen UCI datasets and ten high dimensional biological datasets. The UCI datasets contain a high number of samples and a small or medium number of features. The biological datasets present a high number of features with few samples. The results demonstrate that the use of the ReliefF algorithm and the local search strategy improves the performance of the EO algorithm. The results also show the superiority of the RBEO-LS, among other state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

In this paper, a new compact algorithm is proposed. Two sinusoidal formulas are used to automatically adjust the crossover rate and the mutation scaling factor in the compact Differential Evolution (cDE) metaheuristic. The proposed algorithm, called Compound Sinusoidal cDE, CScDE, is compared to seven state-of-the-art compact algorithms on the well-known BBOB test-bed, the CEC-2014 test suite for continuous optimisation, as well as five real-world optimisation problems chosen from the CEC-2011 benchmarks. The CScDE algorithm outperformed its competitors for most problem categories and over most dimensions. It is also compared to some well-established population-based metaheuristics. • A compact compound sinusoidal differential evolution, CScDE, is proposed. • CScDE uses sinusoidal formulas to adjust the parameters of compact DE. • Both binomial and exponential crossover are tested. • CScDE is applied on 89 reference problems with several dimensions. • CScDE outperformed state-of-the-art compact algorithms. [ABSTRACT FROM AUTHOR]

• Conventional evaporation rate water cycle algorithm (ERWCA). • The proposed MERWCA improves the performance of ERWCA. • To find the optimal solutions for the coordination problem of directional overcurrent relays. • The proposed MERWCA is verified on the CEC'2017 test suite. • MERWCA is assessed using benchmark DIgSILENT PowerFactory. An optimization algorithm called modified evaporation rate water cycle algorithm (MERWCA) is proposed in this paper to find the optimal solutions for the coordination problem of directional overcurrent relays (DOCRs). The proposed MERWCA improves the performance of a conventional evaporation rate water cycle algorithm (ERWCA) by enhancing the balance between exploitation (search locally) and exploration (search globally) phases to find the best optimum solution. MERWCA includes the Opposition Based Learning (OBL) and Levy Flight (LF) component to address the shortcomings that the original ERWCA may exhibit, to avoid falling on the local optimal and improve the convergence rate. The proposed MERWCA is verified on the CEC'2017 test suite and its performance are compared with those of ten common metaheuristic algorithms (MAs). Both MERWCA and ERWCA are evaluated in the case of non-conventional and conventional relay curves. The feasibility of MERWCA technique is assessed using the conventional IEEE 39-bus meshed distribution test system. The results prove the viability of the MERWCA in solving DOCRs coordination problems in both cases (conventional and non-conventional characteristic relay curve) without any miscoordination between DOCRs pair. Moreover, the reduction ratio in minimizing the total operating using MERWCA reaches 46% with respect to the ERWCA and about 66% using the non-conventional relay characteristic. Finally, the MERWCA is assessed using benchmark DIgSILENT PowerFactory. [ABSTRACT FROM AUTHOR]

• We integrate a PSO with a NN and ERNN to form PSO-NN and PSO-ERNN metaheuristic models. • The PSO trains the NN and ERNN to achieve faster convergence and avoid local minima. • The PSO-NN and PSO-ERNN models are evaluated using ten benchmark classification datasets. • The accuracy and computational efficiency of the models are improved. • The PSO-ERNN model outperforms the PSO-NN model for most of the tested datasets. There are several types of neural networks (NNs) that are widely used for data classification tasks. The supervised learning NN is an advanced network with a training algorithm for setting the weights and biases of the network in its training phase. However, traditional training algorithms such as backpropagation have some drawbacks, such as slow convergence speed and falling into local minima, which reduces the performance of the classifier. Therefore, different nature-inspired metaheuristic algorithms are integrated with the NN training algorithms to provide derivative-free solutions for complex classification problems. Consequently, this paper proposes the integration of a particle swarm optimization (PSO) algorithm with an improved Elman recurrent neural network (ERNN) to form a PSO-ERNN metaheuristic model. The key contribution of this study is the development of a new dimensional equation for ERNN architecture and the integration of PSO in ERNN learning to produce the PSO-ERNN model. The PSO is constructed to train the NN and ERNN models to achieve a fast convergence rate and avoid local minima problems. The PSO-ERNN model is validated by comparing it against the standard PSO-NN metaheuristic model and similar models from the literature. The PSO-NN and PSO-ERNN models are tested and evaluated using ten benchmark classification problems of breast cancer, heart, hepatitis, liver, wine, iris, lung cancer, yeast, Pima Indians diabetes, and ionosphere datasets. In the training phase, the results show that the PSO-ERNN model performs better than the PSO-NN model when the training set has a bigger size of samples. In the testing phase, the PSO-ERNN model outperforms the PSO-NN model for all the tested datasets except the lung cancer and yeast datasets, in which the accuracy percentage slightly decreases. In the validation phase, the PSO-ERNN model shows better performance quality in terms of accuracy percentage in six of the tested datasets. The average percentage of the training, testing, and validation accumulation show that the PSO-NN performs better than the PSO-ERNN in the lung cancer (87.27, 83.32), and heart (73.56, 70.64) datasets. On the other hand, the PSO-ERNN performs better than the PSO-NN in the iris (88.18, 86.74), hepatitis (88.60, 87.93), wine (89.16, 86.08), liver (73.56, 70.64), ionosphere (83.98, 78.94), and breast cancer (94.84, 91.17). PSO-NN and PSO-ERNN produce the same average results in the Pima Indians diabetes (84.00, 84.00) and yeast (91.31, 91.30) dataset. These results show clearly that the PSO-ERNN generally outperforms the PSO-NN when considering the overall results of the ten datasets. Nevertheless, the combinations of the PSO-NN and PSO-ERNN are proven to represent consistent and robust classification methods. The computational efficiencies of the training processes in both the PSO-NN and PSO-ERNN models are highly improved when coupled with the PSO. [ABSTRACT FROM AUTHOR]

• Proposed a hybrid algorithm as S-ROA for solving TSP robustly and effectively. • Integration of ROA and SHO accomplished to resolve TSP optimally. • Explorative and exploitative ability of S-ROA found to be remarkably superior. • Performance of S-ROA is verified over benchmark TSP instances. • Competence of S-ROA was proven over contemporary metaheuristic algorithms. Traveling Salesman Problem (TSP) is the combinatorial optimization problem, where a salesman starting from a home city travels all the other cities and returns to the home city in the shortest possible path. TSP is a popular problem because the instances of TSP can be applied to solve real-world problems, the implication of which turns TSP into a typical test bench for performance evaluation of novel algorithms. In the current years, different optimization algorithms inspired by biological groups have become very familiar. A combined intelligence of diverse social insects like bees, ants, birds, termites, fish, etc. has been analyzed to introduce multiple meta-heuristic algorithms in the field of swarm intelligence. The main intent of this paper is to develop a hybrid algorithm for solving the TSP robustly and effectively. In order to attain this challenging point, the objective model considered in this research work is the minimization of the distance of the salesman traveling through entire cities. Here, the optimal solution pertains to solve the TSP is to minimize the distance travelled by the salesman, which is determined based on the new hybrid optimization algorithm. This proposal plans to integrate the two well-performing optimization algorithms like Rider Optimization Algorithm (ROA) and Spotted Hyena Optimizer algorithm (SHO) to frame the new algorithm, Spotted Hyena-based Rider Optimization (S-ROA). Finally, the experimental results obtained by the hybrid algorithm to solve these TSP cases are benchmarked against the results obtained by using state-of-the-art algorithms and prove the competitive performance of the proposed model. [ABSTRACT FROM AUTHOR]

• Adaptive assignment of cowards and heroes via exponential functions controller. • Intensified swap operator around best candidate solutions. • Tent map as the pseudo random generator for agents' initialization. • Excels in nonlinearity and strict avalanche S-Box criteria. This paper introduces a new variant of a metaheuristic algorithm based on Agent Heroes and Cowards Algorithm (AHC), called Adaptive Agent Heroes and Cowards Algorithm (AAHC). The main feature of AAHC is the fact that the algorithm allows adaptive assignment of its population agents into cowards and heroes based on the exponential controlling functions. Furthermore, unlike its predecessor, AAHC also permits systematic manipulation of candidate solutions around the global best agent via the swap operator to boost its search intensification process. Meanwhile, to further enhance the diversification its solution, AHC also exploit the Tent map as the pseudo random generator replacement during its initial population initialization. Experimental results based on the generation of 8 × 8 substitution-box demonstrate that the proposed AAHC outperforms other competing metaheuristic algorithms in two main S-box criteria namely nonlinearity and strict avalanche criteria whilst maintaining commendable performances on bits independence criteria, differential approximation probability, linear approximation probability and transparency order. [ABSTRACT FROM AUTHOR]

• A human mental search is used for cross entropy-based image thresholding. • For improved performance, four modifications are applied on HMS algorithm. • The performance of our algorithm is evaluated based on a variety of metrics. • The obtained results indicate a very competitive performance. The minimum cross-entropy (MCIT) is introduced as a multi-level image thresholding approach, but it suffers from time complexity, in particular, when the number of thresholds is high. To address this issue, this paper proposes a novel MCIT-based image thresholding based on improved human mental search (HMS) algorithm, a recently proposed population-based metaheuristic algorithm to tackle complex optimisation problems. To further enhance the efficacy, we improve HMS algorithm, IHMSMLIT, with four improvements, including, adaptively selection of the number of mental searches instead of randomly selection, proposing one-step k-means clustering for region clustering, updating based on global and personal experiences, and proposing a random clustering strategy. To assess our proposed algorithm, we conduct an extensive set of experiments with several state-of-the-art and the most recent approaches on a benchmark set of images and in terms of several criteria including objective function, peak signal to noise ratio (PSNR), feature similarity index (FSIM), structural similarity index (SSIM), and stability analysis. The obtained results apparently demonstrate the competitive performance of our proposed algorithm. [ABSTRACT FROM AUTHOR]

• Features of initial solutions predict the performance of perturbation algorithms. • Low performance variation within portfolios makes algorithm selection difficult. • Hybrid selection models are useful for portfolios with low performance variation. • Accuracy is not a fair measure to evaluate the performance of algorithm selectors. In this paper, we present the construction process of a per-instance algorithm selection model to improve the initial solutions of Curriculum-Based Course Timetabling (CB-CTT) instances. Following the meta-learning framework, we apply a hybrid approach that integrates the predictions of a classifier and linear regression models to estimate and compare the performance of four meta-heuristics across different problem sub-spaces described by seven types of features. Rather than reporting the average accuracy, we evaluate the model using the closed SBS-VBS gap, a performance measure used at international algorithm selection competitions. The experimental results show that our model obtains a performance of 0.386, within the range obtained by per-instance algorithm selection models in other combinatorial problems. As a result of the process, we conclude that the performance variation between the meta-heuristics has a significant role in the effectiveness of the model. Therefore, we introduce statistical analyses to evaluate this factor within per-instance algorithm portfolios. [ABSTRACT FROM AUTHOR]