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The soil water retention curve (SWRC) has a significant role in determining the unsaturated properties of soil. A stochastic optimisation algorithm named Rao-1 algorithm is employed to estimate the parameters of the SWRC model in this paper. The Rao-1 algorithm is a simple heuristic search algorithm containing only addition and multiplication operations. This paper introduces the method and its application in determining soil water retention this model parameters in detail. In this study, the van Genuchten model is used to depict the SWRC for its good fitting capacity, and the van Genuchten model parameters are determined using Rao-1 algorithm. The feasibility and efficiency of the proposed method are validated via the experimental results of 24 soil samples of 12 soil textural classes. Besides, the performance of Rao-1 algorithm is compared with that of salp swarm algorithm, the particle swarm optimization algorithm, differential evolution algorithm, and RETC program. Through comparative analysis, Rao-1 algorithm outperforms other methods in determining SWRC parameters. [ABSTRACT FROM AUTHOR]

With the rapid growth of the number of web services on the Internet, various service providers provide many similar services with the same function but different quality of service (QoS) attributes. It is a key problem to be solved urgently to select the service composition quickly, meeting the users' QoS requirements from many candidate services. Optimization of web service composition is an NP-hard issue and intelligent optimization algorithms have become the mainstream method to solve this complex problem. This paper proposed a hybrid strategy improved whale optimization algorithm, which is based on the concepts of chaos initialization, nonlinear convergence factor and mutation. By maintaining a balance between exploration and exploitation, the problem of slow or early convergence is overcome to a certain extent. To evaluate its performance more accurately, the proposed algorithm was first tested on a set of standard benchmarks. After, simulations were performed using the real quality of web service dataset. Experimental results show that the proposed algorithm is better than the original version and other meta-heuristic algorithms on average, as well as verifies the feasibility and stability of web service composition optimization. [ABSTRACT FROM AUTHOR]

Meta-heuristic algorithms are widely used in complex problems that cannot be solved by traditional computing methods due to their powerful optimization capabilities. However, for high-complexity problems, the fitness function evaluation may take hours or even days to complete. The surrogate-assisted meta-heuristic algorithm effectively solves this kind of long solution time for the fitness function. Therefore, this paper proposes an efficient surrogate-assisted hybrid meta-heuristic algorithm by combining the surrogate-assisted model with gannet optimization algorithm (GOA) and the differential evolution (DE) algorithm, abbreviated as SAGD. We explicitly propose a new add-point strategy based on information from historical surrogate models, using information from historical surrogate models to allow the selection of better candidates for the evaluation of true fitness values and the local radial basis function (RBF) surrogate to model the landscape of the objective function. The control strategy selects two efficient meta-heuristic algorithms to predict the training model samples and perform updates. A generation-based optimal restart strategy is also incorporated in SAGD to select suitable samples to restart the meta-heuristic algorithm. We tested the SAGD algorithm using seven commonly used benchmark functions and the wireless sensor network (WSN) coverage problem. The results show that the SAGD algorithm performs well in solving expensive optimization problems. [ABSTRACT FROM AUTHOR]

The decomposition-based algorithm, for example, multiobjective evolutionary algorithm based on decomposition (MOEA/D), has been proved effective and useful in a variety of multiobjective optimization problems (MOPs). On the basis of MOEA/D, the MOEA/D-DE replaces the simulated binary crossover (SBX) operator with differential evolution (DE) operator, which is used to enhance the diversity of the solutions more effectively. However, the amplification factor and the crossover probability are fixed in MOEA/D-DE, which would lead to a low convergence rate and be more likely to fall into local optimum. To overcome such a prematurity problem, this paper proposes three different adaptive operators in DE with crossover probability and amplification factors to adjust the parameter settings adaptively. We incorporate these three adaptive operators in MOEA/D-DE and MOEA/D-PaS to solve MOPs and many-objective optimization problems (MaOPs), respectively. This paper also designs a sensitive experiment for the changeable parameter η in the proposed adaptive operators to explore how η would affect the convergence of the proposed algorithms. These adaptive algorithms are tested on many benchmark problems, including ZDT, DTLZ, WFG, and MaF test suites. The experimental results illustrate that the three proposed adaptive algorithms have better performance on most benchmark problems. [ABSTRACT FROM AUTHOR]

Most text clustering algorithms adopt a single criterion optimization approach, which often fails to find good clustering solutions for a wide diversity of datasets with different clustering characteristics. The multi-objective meta-heuristic approach is utilized to seek optimal clustering by maximizing (or minimizing) more than two objective functions. In this paper, we propose a multi-objective memetic differential evolution algorithm (MOMDE) for text clustering. The MOMDE text clustering algorithm combines memetic and differential evolution algorithms to improve the search for optimal clustering by improving the balance between exploitation and exploration. Moreover, a combination with the dominance-based multi-objective approach is employed, which may improve the search for optimal clustering by maximizing or/and minimizing two cluster quality measures. The proposed algorithm is tested on six text clustering datasets from the Laboratory of Computational Intelligence. Our experimental results revealed that the performance of the MOMDE algorithm is better than state-of-the-art text clustering algorithms. Further validation is provided using the F-measure to assess the efficiency of the obtained clustering of MOMDE, whilst the multi-objective performance assessment matrices are used to evaluate the quality of Pareto-optimality. [ABSTRACT FROM AUTHOR]

Aiming at the shortcomings of aquila optimization algorithm（AO）, this paper proposed an aquila optimization algorithm using hybrid search strategy. Firstly, the algorithm introduced dynamic adjustment function to balance global exploration and local exploitation; Secondly, it introduced chaotic adaptive weights to enhance the global search capability of the algorithm and accelerate the convergence speed of the algorithm; Thirdly, it introduced a new individual mutation probability coefficient and an improved differential mutation strategy, and used individuals with better fitness values to lead other individuals in the population to carry out search activities, which maintained the diversity of the population and enhanced the ability of the algorithm to jump out of the local optimum. Through the numerical experiment simulation of 8 benchmark test functions, 10 CEC2019 test functions and 1 engineering application problem, The experimental results show that the algorithm has a significant improvement in global convergence speed and optimization accuracy, and has a better ability to jump out of the local optimum. [ABSTRACT FROM AUTHOR]

Aiming at the shortcomings of normal SPBO, this paper analyzed the psychological characteristics of students ' learning, and put forward an improved student psychology based optimization algorithm ( HSSPBO) using hybrid strategy . Firstly, it took the inverse value of student ' s test scores as the fitness value of the student, and took the fitness value of the best students in the class as the benchmark, and then divided the students in the class into four categories : the best students, the good students, the ordinary students and the students who tried random improvement. Secondly, it used the dynamic switching probability of sine square and cosine square to balance global exploration and local development, so that the global exploration ability and local exploitation ability of the algorithm were improved effectively. Thirdly, by introducing Cauchy mutation strategy to change the local search step size, it improved effectively the local search ability of the algorithm and enhanced the ability of the algorithm to jump out of the local optimization. Finally, the HSSPBO algorithm used Levy flight strategy to make the individual search step length more random and flexible, which effectively enhanced the individual ' s searching ability, and further improved the optimization speed of the algorithm. Through simulation experiments of 12 benchmark functions and comparison with six optimization algorithms, the results show that HSSPBO ' s global search ability has been significantly improved, and it has faster global convergence speed, better optimization accuracy and stability in function optimization. [ABSTRACT FROM AUTHOR]

Assembly sequence planning is one of the key issues in DFA and computer-aided assembly process planning research for concurrent engineering. The purpose of this paper is to solve the problem of insufficient individual intelligence in evolutionary algorithms for assembly sequence planning, and a evolutionary algorithm for assembly sequence planning is designed. In this paper, the particle swarm optimization (PSO) algorithm is used to optimize the hybrid assembly sequence planning and assembly line balance problems. According to the assembly sequence problem, the number of assembly tool changes and the number of assembly orientation changes are transformed into the operation time of the assembly line. At the same time, the transportation of heavy parts in the assembly balance problem is considered. Then, by extracting the connection relationship and information of the parts, the disassembly method is used to inversely obtain the disassembly support matrix, and then, it is used to obtain the priority relationship diagram of the assembly operation tasks that indicate the order constraints of the job tasks on the assembly line. Aiming at the shortcoming that particle swarm optimization algorithm is easy to fall into local optimum, a various population strategy is adopted to shorten the evolution stagnation time, improve the evolution efficiency of particle swarm optimization algorithm, and enhance the optimization ability of the algorithm. Combined with the three evaluation indicators of assembly geometric feasibility, assembly process continuity, and assembly tool change times, a fitness function is constructed to achieve multi-objective optimization. Finally, experiments show that the multi-agent evolutionary algorithm is incorporated into the planning process to obtain an accurate solution through the various population strategy–particle swarm optimization algorithm, which proves the feasibility of the compound algorithm and has better performance in solving assembly sequence planning problems. [ABSTRACT FROM AUTHOR]

In multi-objective bilevel optimisation problems, the upper-level performance of different lower-level optimal solutions may be very different, even though they belong to the same lower-level problem. It may lead to poor optimisation results. Therefore, the lower-level search should search lower-level non-dominated solutions that are also non-dominated in the upper-level objective space. In this paper, we use two populations in the lower-level search. The first population maintains non-dominance and diversity in the lower-level objective space and provides the second population with convergence pressure from the lower level. The second population selects the upper-level non-dominated solutions that are not dominated by the first population in the lower-level objective space, which make the second population maintain the non-dominance at both upper and lower levels. Besides, to improve the search efficiency, we set up the upper-level mating pool to generate the upper-level vectors of offsprings near the upper-level vectors of the better individuals in the current population. To balance convergence and diversity, the selection operator of a decomposition based multi-objective evolutionary algorithm is adopted. The proposed algorithm has been evaluated on a set of benchmark problems and a real-world optimisation problem. Experimental results demonstrate that the proposed algorithm is efficient and effective. [ABSTRACT FROM AUTHOR]

In this paper, weighted differential evolution algorithm (WDE) has been proposed for solving real-valued numerical optimization problems. When all parameters of WDE are determined randomly, in practice, WDE has no control parameter but the pattern size. WDE can solve unimodal, multimodal, separable, scalable, and hybrid problems. WDE has a very fast and quite simple structure, in addition, it can be parallelized due to its non-recursive nature. WDE has a strong exploration and exploitation capability. In this paper, WDE's success in solving CEC' 2013 problems was compared to 4 different EAs (i.e., CS, ABC, JADE, and BSA) statistically. One 3D geometric optimization problem (i.e., GPS network adjustment problem) and 4 constrained engineering design problems were used to examine the WDE's ability to solve real-world problems. Results obtained from the performed tests showed that, in general, problem-solving success of WDE is statistically better than the comparison algorithms that have been used in this paper. [ABSTRACT FROM AUTHOR]

Reentry trajectory optimization is a critical optimal control problem for reusable launch vehicle (RLV) with highly nonlinear dynamic characteristics and complex constraints. In this paper, a hybrid parallel Harris hawks optimization (HPHHO) algorithm is proposed to address the problem. HPHHO aims to enhance the performance of existing Harris hawks optimization (HHO) algorithm by three strategies including oppositional learning, smoothing technique and parallel optimization mechanism. At the beginning of each iteration, the opposite population is calculated from the current population by the oppositional learning strategy. Following that, the individuals in the two populations are arranged in ascending order on the basis of the fitness function values, and the top half of the resulting population is selected as the initial population. The selected initial population is divided into two equal subpopulations which are assigned to the differential evolution and the HHO algorithm, respectively. The both algorithms operate in parallel to search and update the solutions of each subpopulation simultaneously. Then the solutions are smoothed for each iteration by the smoothing technique to reduce fluctuations. As a result, the optimal solution obtained by the parallel optimization mechanism avoids falling into local optima. The performance of HPHHO is evaluated by 4 CEC 2005 benchmark functions and 3 constrained continuous optimal control problems, showing better efficiency and robustness in terms of performance metrics, convergence rate and stability. Finally, the simulation results show that the proposed algorithm is very effective, practical and feasible in solving the RLV reentry trajectory optimization problem. [ABSTRACT FROM AUTHOR]

In this paper, we propose an improved Harris hawk optimizer (IHHO) to overcome the shortcomings of Harris hawk optimizer (HHO) that there is aimless search in the global search process. IHHO is different from HHO in that during the global search process, the position of the Harris hawks is not random but moves according to the position of the best several Harris hawks in the population. The advantage of the new global search process in the IHHO is that it effectively improves the quality of candidate solutions. We use the proposed algorithm to solve job-shop scheduling problem, which is an NP-hard problem and also a very challenging optimization problem, because it is difficult to find a reasonable candidate solution in polynomial time. Many researchers have tried various methods to solve it, but failed to get an effective result. Therefore, we use it to verify the performance of the proposed algorithm. The experimental results of IHHO are compared with those of improved genetic algorithm, differential evolution and HHO. It can be seen from the experimental results that IHHO has obvious advantages compared with the results of other comparison algorithms in terms of convergence accuracy, convergence speed, stability and running time. The experimental results of Wilcoxon's rank sum test also show that the IHHO is essentially different from other comparison algorithms, which shows that the optimization performance of the IHHO is meaningful. [ABSTRACT FROM AUTHOR]

The fitness landscape is a theory applied to the evolutionary dynamics of biological evolution to explain the behavior of evolutionary algorithms in the solution of optimization problems. With the continuous advancement of evolutionary algorithm optimization, a fitness landscape can present more abundant feature information, such as the local fitness, fitness distance correlation, and landscape roughness. These landscape features reflect the optimal solution distribution, quantity, and local unimodal topology of the optimization problem from various angles. This paper expresses the adaptability landscape features of typical optimization problems, engages in a quantitative analysis of the fitness distance correlation information, evaluates the difficulty of solving the problem within the search space, and obtains the correlation degree classification result. The search strategy adapts the mixed mutation and the fitness distance correlation for differential evolution. Empirical studies show that, the fitness distance correlation search strategy for the differential evolution algorithm can avoid falling into the local optimum, improve accuracy and convergence, and solve the single-objective optimization problem in a more comprehensive manner. [ABSTRACT FROM AUTHOR]

In this paper, a new design approach of approximately linear phase infinite impulse response (IIR) low pass digital differentiator (LPDD) is proposed and studied. The proposed design is based on a transfer function that has a numerator with anti-symmetric coefficients. To better control the magnitude response of the designed LPDD, the differential evolution (DE) optimization algorithm is used to find the coefficients of the transfer function that meets an appropriate pass band and stop band edge frequencies. The use of appropriate pass band and stop band edge frequencies gives the designer of the LPDD direct control on the width of the transition band. The designed LPDD using the proposed approach has approximately linear phase and much better magnitude response than that of the IIR LPDDs designed using other techniques reported in literature. In addition, the designed LPDD using the proposed approach has steeper roll-off magnitude response and narrower transition band than that of designed IIR and high order FIR LPDDs available in the literature. [ABSTRACT FROM AUTHOR]

In a textile mill, ring and rotor spinning processes are the main technologies employed to convert cotton fibres into yarns with desirable qualities. It has been observed that in order to produce yarns having satisfactory quality characteristics, those spinning processes need to be run while setting different input variables (control parameters) at their optimal operating levels. In this paper, the optimal parametric combinations of back and front zone variables in a ring frame, and input variables in a rotor spinning process are determined based on teaching-learning-based optimization algorithm. The optimization performance of this algorithm is also compared with four other techniques, e.g., firefly algorithm, differential evolution algorithm, cuckoo search algorithm and quantum particle swarm optimization algorithm with respect to accuracy and consistency of the derived solutions. This optimization technique can thus be effectively applied to any of the intermediate processes in a textile mill to obtain the best combinations of different input variables so as to achieve the target quality characteristics. [ABSTRACT FROM AUTHOR]

The cuckoo search (CS) algorithm is an effective optimization algorithm, but it is prone to stagnation in suboptimality because of some limitations in its exploration mechanisms. This paper introduces a variation of CS called exploratory CS (ECS), which incorporates three modifications to the original CS algorithm to enhance its exploration capabilities. First, ECS uses a special type of opposition-based learning called refraction learning to improve the ability of CS to jump out of suboptimality. Second, ECS uses the Gaussian perturbation to optimize the worst candidate solutions in the population before the discard step in CS. Third, in addition to the Lévy flight mutation method used in CS, ECS employs two mutation methods, namely highly disruptive polynomial mutation and Jaya mutation, to generate new improved candidate solutions. A set of 14 widely used benchmark functions was used to evaluate and compare ECS to three variations of CS:CS with Lévy flight (CS), CS with highly disruptive polynomial mutation (CS10) and CS with pitch adjustment mutation (CS11). The overall experimental and statistical results indicate that ECS exhibits better performance than all of the tested CS variations. Besides, the single-objective IEEE CEC 2014 functions were used to evaluate and compare the performance of ECS to six well-known swarm optimization algorithms: CS with Lévy flight, Grey wolf optimizer (GWO), distributed Grey wolf optimizer (DGWO), distributed adaptive differential evolution with linear population size reduction evolution (L-SHADE), memory-based hybrid Dragonfly algorithm and Fireworks algorithm with differential mutation. Interestingly, the results indicate that ECS provides competitive performance compared to the tested six well-known swarm optimization algorithms. [ABSTRACT FROM AUTHOR]

Hybridization of meta-heuristic algorithms plays a major role in the optimization problem. In this paper, a new hybrid meta-heuristic algorithm called hybrid pathfinder algorithm (HPFA) is proposed to solve the optimal reactive power dispatch (ORPD) problem. The superiority of the Differential Evolution (DE) algorithm is the fast convergence speed, a mutation operator in the DE algorithm incorporates into the pathfinder algorithm (PFA). The main objective of this research is to minimize the real power losses and subject to equality and inequality constraints. The HPFA is used to find optimal control variables such as generator voltage magnitude, transformer tap settings and capacitor banks. The proposed HPFA is implemented through several simulation cases on the IEEE 118-bus system and IEEE 300-bus power system. Results show the superiority of the proposed algorithm with good quality of optimal solutions over existing optimization techniques, and hence confirm its potential to solve the ORPD problem. [ABSTRACT FROM AUTHOR]

Van Genuchten (VG) equation is the most commonly used equation of soil moisture characteristic curve, and the accuracy of its parameters directly affects the calculation accuracy of soil moisture motion equation. In order to obtain the parameters of the equation more accurately, this paper establishes an optimization model of the VG equation parameters. This optimization model uses the advantages of the cuckoo search algorithm and the differential evolution algorithm to combine the two into a new hybrid cuckoo search algorithm, namely DECS algorithm, and uses this algorithm to solve the parameter optimization problem of VG equation of soil moisture characteristic curve. By collecting and analyzing the relevant experimental data of various soil qualities, the dehumidification and moisture absorption curves of three different soil qualities were selected for simulation calculation. The results show that in the experiment of solving the parameter estimation problem of the VG equation, the DECS hybrid algorithm has better exploration and development capabilities than the cuckoo search algorithm. The hybrid algorithm has relatively significant performance in terms of calculation accuracy and convergence. [ABSTRACT FROM AUTHOR]

Medical diagnosis has seen a tremendous advancement in the recent years due to the advent of modern and hybrid techniques that aid in screening and management of the disease. This paper figures a predictive model for detecting neurodegenerative diseases like glaucoma, Parkinson's disease and carcinogenic diseases like breast cancer. The proposed approach focuses on enhancing the efficiency of adaptive neuro-fuzzy inference system (ANFIS) using a modified glowworm swarm optimization algorithm (M-GSO). This algorithm is a global optimization wrapper approach that simulates the collective behavior of glowworms in nature during food search. However, it still suffers from being trapped in local minima. Hence in order to improve glowworm swarm optimization algorithm, differential evolution (DE) algorithm is utilized to enhance the behavior of glowworms. The proposed (DE–GSO–ANFIS) approach estimates suitable prediction parameters of ANFIS by employing DE–GSO algorithm. The outcomes of the proposed model are compared with traditional ANFIS model, genetic algorithm-ANFIS (GA-ANFIS), particle swarm optimization-ANFIS (PSO-ANFIS), lion optimization algorithm-ANFIS (LOA-ANFIS), differential evolution-ANFIS (DE-ANFIS) and glowworm swarm optimization (GSO). Experimental results depict better performance and superiority of the DE–GSO–ANFIS over the similar methods in predicting medical disorders. [ABSTRACT FROM AUTHOR]

Optimization for all disciplines is very important and applicable. Optimization has played a key role in practical engineering problems. A novel hybrid meta-heuristic optimization algorithm that is based on Differential Evolution (DE), Gradient Evolution (GE) and Jumping Technique named Differential Gradient Evolution Plus (DGE+) are presented in this paper. The proposed algorithm hybridizes the above-mentioned algorithms with the help of an improvised dynamic probability distribution, additionally provides a new shake off method to avoid premature convergence towards local minima. To evaluate the efficiency, robustness, and reliability of DGE+ it has been applied on seven benchmark constraint problems, the results of comparison revealed that the proposed algorithm can provide very compact, competitive and promising performance. [ABSTRACT FROM AUTHOR]

In order to solve the problem that users deep interests and preferences cannot excavate in the recommendation. algorithm, which leads to low extraction accuracy and high time complexity of similar users clustering accuracy, this paper proposed a clustering optimization recommendation algorithm (MT-QRPD) based on the evaluation quantization model to optimize differential evolution. Firstly, it used the feature timing of BiGRU network and the strong local feature effectiveness of CNN network to extract the comment depth features, and used the multi-dimensional semantic feature screening of multi-head attention mechanism to mine the comment depth semantic features. Then, through the nonlinear transformation of the multi layer perceptron carried out the multi-feature fusion to achieve accurate quantification. Finally, the algorithm optimized the clustering of similar users with PCA, and completed the project recommendation. The result of experiments shows that the pro posed algorithm can improve the accuracy, time complexity and recommendation performance. [ABSTRACT FROM AUTHOR]

This paper presents a novel differential evolution (DE) algorithm, with its improved version (IDE) for the benchmark functions and the optimal reactive power dispatch (ORPD) problem. Minimization of the total active power loss is usually considered as the objective function of the ORPD problem. The constraints involved are generators, transformers tapings, shunt reactors, and other reactive power sources. The aim of this study is to discover the best vector of control variables to minimize power loss, under the premise of considering the constraints system. In the proposed IDE, a new initialization strategy is developed to construct the initial population for guaranteeing its quality and simultaneously maintaining its diversity. In addition, to enhance the convergence characteristic of the original DE, two kinds of self-adaptive adjustment strategies are employed to update the scaling factor and the crossover factor, respectively, in which the detailed information about the two factors can be exchanged for each generation dynamically. Numerical applications of different cases are carried out on several benchmark functions and two standard IEEE systems, i.e., 14-bus and 30-bus test systems. The results achieved by using the proposed IDE, compared with other optimization algorithms, are discussed and analyzed in detail. The obtained results demonstrated that the proposed IDE can successfully be used to deal with the ORPD problem. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to examine the design and control of a symmetric multilevel inverter (MLI) using grey wolf optimization and differential evolution algorithms. Design/methodology/approach: The optimal modulation index along with the switching angles are calculated for an 11 level inverter. Harmonics are used to estimate the quality of output voltage and measuring the improvement of the power quality. Findings: The simulation is carried out in MATLAB/Simulink for 11 levels of symmetric MLI and compared with the conventional inverter design. A solar photovoltaic array-based experimental setup is considered to provide the input for symmetric MLI. Field Programmable Gate Array (FPGA) based controller is used to provide the switching pulses for the inverter switches. Originality/value: Attempted to develop a system with different optimization techniques. [ABSTRACT FROM AUTHOR]

Black box attack is mainly focuses on the decision information, but there are many query times and the selection of sensitive pixels is difficult. Therefore, this paper proposed a defect sample generation algorithm based on differential evolution. By defining the black box attack as an unconstrained optimization problem, the algorithm used differential evolution to search image sensitive points, and optimized the loss function of decision definition and gradient calculation based on the deep learning model to achieve an effective black box attack. On MNIST and CIF ARlO datasets, the average query times of the proposed algorithm decreased by 28.3% and 14.8% respectively under the same attack success rate. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the WOA was easy to fall into local optimum and low convergence precision, this paper proposed an improved whale optimization algorithm (IWOA). The algorithm initialized the population by quasi-reverse learning methods and improved the diversity of the population. Then the algorithm modified the linear convergence factor to a nonlinear convergence factor, which was beneficial to balance the global search ability and local development ability. In addition, the algorithm improved the local search ability of the whale optimization algorithm by increasing the adaptive weight and imp roved convergence precision. Finally, the algorithm adjusted the whale optimization algorithm in time by a random differential mutation strategy to avoid falling into the local optimum. It selected nine benchmark functions in the experiment, and iterated all the algorithms 30 times. The improved whale optimization algorithm compared to the original whale optimization algorithm and five improved whale optimization algorithms, the results show that the mean and standard deviation of the algorithm are better than other algorithms, the convergence curve of the algorithm is also superior to most other algorithms. It shows that the imp roved whale optimization algorithm has the best convergence accuracy and algorithm stability, and the convergence speed is significantly faster than most other improved whale optimization algorithms. [ABSTRACT FROM AUTHOR]

This study presented a new multi-species binary coded algorithm, Mendelian evolutionary theory optimization (METO), inspired by the plant genetics. This framework mainly consists of three concepts: first, the "denaturation" of DNA's of two different species to produce the hybrid "offspring DNA". Second, the Mendelian evolutionary theory of genetic inheritance, which explains how the dominant and recessive traits appear in two successive generations. Third, the Epimutation, through which organism resist for natural mutation. The above concepts are reconfigured in order to design the binary meta-heuristic evolutionary search technique. Based on this framework, four evolutionary operators—(1) Flipper, (2) Pollination, (3) Breeding, and (4) Epimutation—are created in the binary domain. In this paper, METO is compared with well-known evolutionary and swarm optimizers: (1) binary hybrid GA, (2) bio-geography-based optimization, (3) invasive weed optimization, (4) shuffled frog leap algorithm, (5) teaching–learning-based optimization, (6) cuckoo search, (7) bat algorithm, (8) gravitational search algorithm, (9) covariance matrix adaptation evolution strategy, (10) differential evolution, (11) firefly algorithm and (12) social learning PSO. This comparison is evaluated on 30 and 100 variables benchmark test functions, including noisy, rotated, and hybrid composite functions. Kruskal–Wallis statistical rank-based nonparametric H-test is utilized to determine the statistically significant differences between the output distributions of the optimizer, which are the result of the 100 independent runs. The statistical analysis shows that METO is a significantly better algorithm for complex and multi-modal problems with many local extremes. [ABSTRACT FROM AUTHOR]

Scheduling is a fundamental factor in managing the network resource of the Internet of things. For IoT systems such as production lines, to operate effectively, it is necessary to find an intelligent management scheme, i.e., schedule, for network resources. In this study, we focus on multiskill resource-constrained project scheduling problem (MS-RCPSP), a combinational optimization problem that has received extensive attention from the research community due to its advantages in network resource management. In recent years, many approaches have been utilized for solving this problem such as genetic algorithm and ant colony optimization. Although these approaches introduce various optimization techniques, premature convergence issue also occurs. Moreover, previous studies have only been verified on simulation data but not on real data of factories. This paper formulated the MS-RCPSP and then proposed a novel algorithm called DEM to solve it. The proposed algorithm was developed based on the differential evolution metaheuristic. Besides, we build the reallocate function to improve the solution quality so that the proposed algorithm converges rapidly to global extremum while also avoiding getting trapped in a local extremum. To evaluate the performance of the proposed algorithm, we conduct the experiment on iMOPSE, the simulated dataset used by previous authors in their research studies. In addition, DEM was verified on real dataset supported by a famous textile industry factory. Experimental results on both simulated data and real data show that the proposed algorithm not only finds a better schedule compared with related works but also can reduce the processing time of the production line currently used at the textile industry factory. [ABSTRACT FROM AUTHOR]

In this paper, a new comprehensive learning gravitational search algorithm (CLGSA) is proposed to enhance the performance of basic GSA. The proposed algorithm is a new kind of intelligent optimization algorithm which has better ability to choose good elements. An intensive comprehensive learning methodology is proposed to enrich the optimization ability of the GSA. The efficiency of the proposed algorithm was evaluated by 28 benchmark functions which have been proposed in IEEE-CEC 2013 sessions. The results are compared with eight state-of-the-art algorithms IPOP, BIPOP, NIPOP, NBIPOP, DE/rand, SPSRDEMMS, SPSO-2011 and GSA. A variety of ways are considered to examine the ability of the proposed technique in terms of convergence ability, success rate and statistical behavior of algorithm over dimensions 10, 30 and 50. Apart from experimental studies, theoretical stability of the proposed CLGSA is also proved. It was concluded that the proposed algorithm performed efficiently with good results. [ABSTRACT FROM AUTHOR]

Differential Evolution (DE) algorithm generates a population of individuals by encoding with a floating point vector, and it is a simple and effective population-based stochastic optimization algorithm for global optimization of continuous space. Because of its excellent performance, DE variants can be applied in a wide range of applications in science and engineering. However, the performance of DE is sensitive to the choice of trial vector generation strategy and the associated control parameters. Therefore, it is necessary to choose appropriate mutation strategy and control parameters when tackling optimization applications. In this paper, an adaptive update mechanism is proposed to update control parameters F and Cr. The experimental results are verified on the CEC 2013 test suite which contains 28 benchmark functions for the evaluation of single objective real parameter optimization. The proposed algorithm is compared with jDE, iwPSO and ccPSO, and experiment results show its good performance. [ABSTRACT FROM AUTHOR]

Differential evolution (DE) is an efficient population-based search algorithm for solving numerical optimization problems. However, the performance of DE is very sensitive to the choice of mutation strategies and their associated control parameters. In this paper, we propose a self-adaptive multi-population differential evolution algorithm, called SAMDE. The population is randomly divided into three equally sized sub-populations, each with different mutation strategies. At the end of each generation, all sub-populations are updated independently and recombined. Each sub-population uses an adaptive mechanism for selecting how current generation control parameters are generated. An improved mutation strategy, "rand assemble/1", is proposed, its base vector is composed proportionally of three randomly selected individuals. The performance of SAMDE is evaluated on the suite of CEC 2005 benchmark functions. A comparative study is carried out with other state-of-the-art optimization techniques. The results show that SAMDE has a competitive performance compared to several other efficient DE variants. [ABSTRACT FROM AUTHOR]

The set-union knapsack problem (SUKP) is a variation of the 0–1 knapsack problem (KP) in which each item is a set of elements, each item has a nonnegative value, and each element has a nonnegative weight. The weight of one item is given by the total weight of the elements in the union of the items' sets. The SUKP accommodates a number of real-life applications and is more complicated and computationally difficult than the 0–1 KP. In this paper, we propose a novel hybrid Jaya algorithm with double coding (DHJaya) to solve the SUKP. In the DHJaya, double coding is used to represent the individual, which includes the solutions for solving the SUKP by adopting a mapping function. The Jaya algorithm and differential evolution algorithm are combined to improve the exploration ability. To enhance the exploitation ability, the Cauchy mutation is performed on some individuals. Meanwhile, an improved repairing and optimization algorithm (MS-GROA) is proposed to repair the infeasible solutions and optimize the feasible solutions. We test the DHJaya using three sets of SUKP instances to demonstrate its efficiency, and the obtained results are compared with those in the previous study. Extensive experiments show a remarkable performance of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper corroborated the hybrid Alopex based Differential Evolution Craziness based Particle Swarm Optimization algorithm (ADECRPSO) over DE, ADE, PSO, and CRPSO algorithms to pursuit the gain parameters of the PID and Fuzzy PID (FPID) controller. In a two-area thermal-hydro-diesel power system, primacy of FPID controller is endorsed with PID controller tuned with assorted optimization techniques. The hybrid ADECRPSO algorithm is affirmed over the above mentioned algorithms to tune PID controller in a two-area hydro-thermal system. PSO, DE, CRPSO, ADE, and ADECRPSO are executed individually to optimize the controller to enhance the transient analysis by conceding undershoot, overshoot, and settling time of the system. The compilation of advantages of alopex based DE and craziness based PSO causes an adequate hybrid algorithm, which enhances the performance of Automatic Generation Control (AGC). The step load uprise in area-1 is imposed to observe the activities of AGC. Undeniably, FPID controller optimized by ADECRPSO commits superior performance over PSO, DE, CRPSO, and ADE optimized controller as proposed AGC system. The FPID controller optimized by ADECRPSO, ADE, and CRPSO is realized in real time environment (OPAL RT OP5600). So, the modified mutation of DE by alopex scheme enhances the potentiality to tune the system variables. [ABSTRACT FROM AUTHOR]

In this paper, six metaheuristic algorithms, in the form of artificial bee colony optimization, ant colony optimization, particle swarm optimization, differential evolution, firefly algorithm and teaching-learning-based optimization techniques are applied for parametric optimization of a multi-pass face milling process. Using those algorithms, the optimal values of cutting speed, feed rate and depth of cut for both roughing and finishing operations are determined for having minimum total production time and total production cost. It is observed that the teaching-learning-based optimization algorithm outperforms the others with respect to accuracy and consistency of the derived solutions as well as computational speed. Two statistical tests, i.e. paired t-test and Wilcoxson signed rank test also confirm its superiority over the remaining algorithms. Finally, these metaheuristics are employed for multi-objective optimization of the considered multi-pass milling process while concurrently minimizing both the objectives. [ABSTRACT FROM AUTHOR]

This paper is concerned with the application of computational intelligence techniques to the conceptual design and development of a large-scale floating settlement. The settlement in question is a design for the area of Urla, which is a rural touristic region located on the west coast of Turkey, near the metropolis of Izmir. The problem at hand includes both engineering and architectural aspects that need to be addressed in a comprehensive manner. We thus adapt the view as a multi-objective constrained real-parameter optimization problem. Specifically, we consider three objectives, which are conflicting. The first one aims at maximizing accessibility of urban functions such as housing and public spaces, as well as special functions, such as a marina for yachts and a yacht club. The second one aims at ensuring the wind protection of the general areas of the settlement, by adequately placing them in between neighboring land masses. The third one aims at maximizing visibility of the settlement from external observation points, so as to maximize the exposure of the settlement. To address this complex multi-objective optimization problem and identify lucrative alternative design solutions, a multi-objective harmony search algorithm (MOHS) is developed and applied in this paper. When compared to the Differential Evolution algorithm developed for the problem in the literature, we demonstrate that MOHS achieves competitive or slightly better performance in terms of hyper volume calculation, and gives promising results when the Pareto front approximation is examined. [ABSTRACT FROM AUTHOR]

Based on Differential Evolution and Cuckoo Search, a hybrid optimization algorithm, called DDECS, is proposed in this paper for solving the ship pipe route layout optimization problem. The purpose of DDECS algorithm is to quickly and accurately optimize the ship pipe route under various kinds of constraints. Firstly, this paper proposed a vector coding method for ship pipe routes by studying the existing coding methods and ship pipe routes' attributes. The proposed vector coding method has a concise structure, strong compatibility and is independent of the gridding space. And then, the Discrete Hybrid Differential Evolution and Cuckoo Search (DDECS) algorithm was implemented by using the proposed vector coding. The DDECS algorithm has few parameters and high computational efficiency, and it can effectively find the global optimal solution by maintaining the diversity of the population. Finally, three comparative simulations were presented to validate the effectiveness and engineering practicality of DDECS algorithm. The results showed that, the proposed DDECS algorithm can get an optimization scheme with fewer elbows and more remaining areas, which is significant for the layout optimization of the ship pipe route. • According to the pipe route attributes, this paper proposes a concise, universal, and independent coding for ship pipes. • Combining Differential Evolution and Cuckoo Search, a hybrid algorithm is proposed for optimizing the ship pipe route. • Three comparative simulations are used to reveal that the proposed coding and algorithm have feasibility and practicability. [ABSTRACT FROM AUTHOR]

This paper develops a robust strategy based on integrating three mutation phases and adapted control parameters into the Adaptive Guided Differential Evolution algorithm called (mAGDE) to improve diversity and exploration of the original AGDE. The mAGDE performance is evaluated using IEEE CEC'2020 test suite. Furthermore, the mAGDE is employed to identify the solid oxide fuel cell (SOFC) model optimal parameters. Two modes of SOFC operation are investigated, steady and transient states. The results obtained from the proposed mAGDE are compared with a number of recent, well-established and reputed meta-heuristics, including Particle swarm optimization, Teaching learning-based optimization, Whale optimization algorithm, Harris hawks optimization, Marine predators algorithm, Archimedes optimization algorithm, Differential evolution, and the original AGDE. Additionally, the statistical parameters that measure the performance of the proposed optimizer and the other competitors are calculated. The main finding demonstrated the preference and robustness of the suggested mAGDE in constructing the SOFC circuit that closely converges to the actual one. During the steady-state operation, the best fitness value obtained via the suggested mAGDE for operation at 1273 K is 2.2995E−06, while in the transient-state operation, the best SMSE is 1.04. The average cost function is decreased by 43.33% compared to the one obtained by the original AGDE. From the aforementioned assessments, it can be concluded that the proposed mAGDE is outstanding and promising. • This paper introduces a modified mAGDE based on the integration of a modified three-phases AGDE with the DE_SPA algorithm. • A modified three-phases AGDE has been introduced to maintain diversity and convergence of the evolutionary process. • The CEC'2020 test suite problems are used to demonstrate the performance mAGDE against other competitors. • Applying for first time mAGDE to identify the optimal parameters of SOFC model. • The robustness of the proposed strategy-based mAGDE is proved. [ABSTRACT FROM AUTHOR]

Differential evolution (DE) is one of the most powerful and versatile evolutionary algorithms for efficiently solving complex real-world optimization problems in recent years. Since its introduction in 1995, the research focus in DE has mostly been on the variant side with so many new algorithms proposed based on the original DE algorithm. However, each new algorithm is only suitable for certain fitness landscapes, and, therefore, some types of optimization problems cannot be solved efficiently. To tackle this issue, this paper presents a new self-feedback DE algorithm, named the SFDE; its optimal variation strategy is selected by extracting the local fitness landscape characteristics in each generation population and combing the probability distributions of unimodality and multimodality in each local fitness landscape. The proposed algorithm is tested on a suite of 17 benchmark functions, and the experimental results demonstrated its advantages in a high search dimension in that it can ensure that the population moves to a better fitness landscape, then speeds up convergence to the global optimum, and avoids falling into local optima. [ABSTRACT FROM AUTHOR]

Highlights • A hybrid DE/WOA method is proposed for parameter extraction of PV models. • DE/WOA can combine the exploration of DE with the exploitation of WOA well. • Thirteen numerical benchmark functions are firstly employed to verify DE/WOA. • DE/WOA is then applied to three different benchmark test PV models. • DE/WOA is finally applied to a practical PV power station in China. Abstract Parameter extraction of solar photovoltaic (PV) models is a typical complex nonlinear multivariable strongly coupled optimization problem. The original differential evolution (DE) is good at exploring the search space and locating the region of global optimum, but it is slow at exploitation of the solutions. Quite the opposite, the original whale optimization algorithm (WOA) is good at exploiting the population information, but it easily suffers from premature convergence. In such a context, in this paper, an effective hybrid method named DE/WOA by combining the exploration of DE with the exploitation of WOA is proposed for extracting the accurate parameters of PV models. A set of 13 numerical benchmark functions with different characteristics is firstly employed to verify the performance of DE/WOA. Then, DE/WOA is applied to parameter extraction of three PV models, i.e., single diode, double diode, and PV module models. Finally, DE/WOA is implemented to a practical PV power station in the Guizhou Power Grid of China to further validate its effectiveness under different irradiances, temperatures, and dynamic weather conditions. All the experimental results comprehensively demonstrate that DE/WOA performs significantly better than the original DE, WOA, and five advanced variants of them and is highly competitive with some recently-proposed parameter extraction methods. [ABSTRACT FROM AUTHOR]

Clustering Search ( *CS) has been proposed as a generic way of combining search metaheuristics with clustering to detect promising search areas before applying local search procedures. The clustering process may keep representative solutions associated with different search subspaces. In this paper, new approaches are proposed, based on *CS, as an Artificial Bee Colony-based one, which detects promising food sources alike *CS approaches. The other new *CS approach is based on Differential Evolution (DE) algorithm. The DE is just a CS component (the evolutionary algorithm), different from ABC-based approach, called Artificial Bee Clustering Search (ABCS). ABCS tries to find promising solutions using some concepts from CS. The proposed hybrid algorithms, performing a Hooke and Jeeves-based local, are compared to another hybrid approaches, exploring an elitist criteria to apply local search. The experiments show that the proposed ABCS and Differential Evolutionary Clustering Search (DECS) are competitive for the majority continuous optimization functions benckmarks selected in this paper. [ABSTRACT FROM AUTHOR]

This paper proposes an improved memetic algorithm relying on ring neighborhood topology for constrained optimization problems based on our previous work in Cai et al. (Soft Comput (in press), ). The main motivation of using ring neighborhood topology is to provide a good balance between effective exploration and efficient exploitation, which is a very important design issue for memetic algorithms. More specifically, a novel variant of invasive weed optimization (IWO) as the local refinement procedure is proposed in this paper. The proposed IWO variant adopts a neighborhood-based dispersal operator to achieve more fine-grained local search through the estimation of neighborhood fitness information relying on the ring neighborhood topology. Furthermore, a modified version of differential evolution (DE), known as 'DE/current-to-best/1', is integrated into the improved memetic algorithm with the aim of providing a more effective exploration. Performance of the improved memetic algorithm has been comprehensively tested on 13 well-known benchmark test functions and four engineering constrained optimization problems. The experimental results show that the improved memetic algorithm obtains greater competitiveness when compared with the original memetic approach Cai et al. in (Soft Comput (in press), ) and other state-of-the-art algorithms. The effectiveness of the modification of each component in the proposed approach is also discussed in the paper. [ABSTRACT FROM AUTHOR]

In this paper, a new color watermarking algorithm based on differential evolution is proposed. A color host image is first converted from RGB space to YIQ space, which is more suitable for the human visual system. Then, apply three-level discrete wavelet transformation to luminance component Y and generate four different frequency sub-bands. After that, perform singular value decomposition on these sub-bands. In the watermark embedding process, apply discrete wavelet transformation to a watermark image after the scrambling encryption processing. Our new algorithm uses differential evolution algorithm with adaptive optimization to choose the right scaling factors. Experimental results show that the proposed algorithm has a better performance in terms of invisibility and robustness. [ABSTRACT FROM AUTHOR]

Different mutation strategies and control parameters settings directly affect the performance of differential evolution (DE) algorithm. In this paper, a self-adaptive differential evolution algorithm with improved mutation strategy (IMSaDE) is proposed to improve optimization performance of DE. IMSaDE improves the “DE/rand/2” mutation strategy by incorporating elite archive strategy and control parameters adaptation strategy. Both strategies diversify the population and improve the convergence performance of the algorithm. IMSaDE was compared with eleven DE algorithms and six non-DE algorithms by using a set of 20 benchmark functions taken from the literature. Experimental results show that the overall performance of IMSaDE is better than the other competitors. In addition, the size of elite population has a significant impact on the performance of IMSaDE. [ABSTRACT FROM AUTHOR]

This paper presents enhanced fitness-adaptive differential evolution algorithm with novel mutation (EFADE) for solving global numerical optimization problems over continuous space. A new triangular mutation operator is introduced. It is based on the convex combination vector of the triplet defined by the three randomly chosen vectors and the difference vectors between the best, better and the worst individuals among the three randomly selected vectors. Triangular mutation operator helps the search for better balance between the global exploration ability and the local exploitation tendency as well as enhancing the convergence rate of the algorithm through the optimization process. Besides, two novel, effective adaptation schemes are used to update the control parameters to appropriate values without either extra parameters or prior knowledge of the characteristics of the optimization problem. In order to verify and analyze the performance of EFADE, numerical experiments on a set of 28 test problems from the CEC2013 benchmark for 10, 30 and 50 dimensions, including a comparison with 12 recent DE-based algorithms and six recent evolutionary algorithms, are executed. Experimental results indicate that in terms of robustness, stability and quality of the solution obtained, EFADE is significantly better than, or at least comparable to state-of-the-art approaches with outstanding performance. [ABSTRACT FROM AUTHOR]

It is known that the optimization of the Earth-Moon low-energy transfer trajectory is extremely sensitive with the initial condition chosen to search. In order to find the proper initial parameter values of Earth-Moon low-energy transfer trajectory faster and obtain more accurate solutions with high stability, in this paper, an efficient hybridized differential evolution (DE) algorithm with a mix reinitialization strategy (DEMR) is presented. The mix reinitialization strategy is implemented based on a set of archived superior solutions to ensure both the search efficiency and the reliability for the optimization problem. And by using DE as the global optimizer, DEMR can optimize the Earth-Moon low-energy transfer trajectory without knowing an exact initial condition. To further validate the performance of DEMR, experiments on benchmark functions have also been done. Compared with peer algorithms on both the Earth-Moon low-energy transfer problem and benchmark functions, DEMR can obtain relatively better results in terms of the quality of the final solutions, robustness, and convergence speed. [ABSTRACT FROM AUTHOR]

The differential evolution, one of the most powerful nature inspired algorithm is used to solve the real world problems. This algorithm takes minimum number of function evaluations to reach near to global optimum solution. Although its performance is very good, yet it suffers from the problem of stagnation. In this paper, some new mutation strategies are proposed to improve the performance of differential evolution (DE). The proposed method adds one more vector named as Homeostasis mutation vector in the existing mutation vectors to provide more bandwidth for selecting effective mutant solutions. The proposed approach provides more promising solutions to guide the evolution and helpsDEescaping the situation of stagnation. Performance of proposed algorithm is compared with other state-of-the-art algorithms on COCO (Comparing Continuous Optimizers) framework. The result verifies that our proposed Homeostasis mutation strategy outperform most of the state-of-the-art DE variants and other state-of-the-art population based optimization algorithms. [ABSTRACT FROM AUTHOR]