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Trade credit is a significant form of short-term financing in the real business situation. This study proposes a practical multi-warehouse joint replenishment and delivery (MJRD) problem under trade credit in accordance with the realistic situation. The goal of the MJRD is to find the reasonable basic replenishment cycle time, the joint replenishment frequency, the delivery frequency, and the assignment information of suppliers to minimize the total cost. Five intelligent algorithms, which include a differential evolution algorithm, genetic algorithm, adaptive hybrid differential evolution algorithm, arithmetic optimization algorithm (AOA), and hybrid arithmetic optimization algorithm (HAOA), are designed to find a solution to this MJRD problem under trade credit. The results of several experiments show that HAOA is effective in solving the proposed MJRD. Compared with AOA, the best improvement is 46.66%. HAOA is a satisfactory algorithm for the proposed MJRD under trade credit. [ABSTRACT FROM AUTHOR]

For any machine learning model, finding the optimal hyperparameter setting has a direct and significant impact on the model's performance. In this paper, we discuss different types of hyperparameter optimization techniques. We compare the performance of some of the hyperparameter optimization techniques on image classification datasets with the help of AutoML models. In particular, the paper studies Bayesian optimization in depth and proposes the use of genetic algorithm, differential evolution and covariance matrix adaptation—evolutionary strategy for acquisition function optimization. Moreover, we compare these variants of Bayesian optimization with conventional Bayesian optimization and observe that the use of covariance matrix adaptation—evolutionary strategy and differential evolution improves the performance of standard Bayesian optimization. We also notice that Bayesian optimization tends to perform poorly when genetic algorithm is used for acquisition function optimization. [ABSTRACT FROM AUTHOR]

Feature selection (FS) is one of the basic data preprocessing steps in data mining and machine learning. It is used to reduce feature size and increase model generalization. In addition to minimizing feature dimensionality, it also enhances classification accuracy and reduces model complexity, which are essential in several applications. Traditional methods for feature selection often fail in the optimal global solution due to the large search space. Many hybrid techniques have been proposed depending on merging several search strategies which have been used individually as a solution to the FS problem. This study proposes a modified hunger games search algorithm (mHGS), for solving optimization and FS problems. The main advantages of the proposed mHGS are to resolve the following drawbacks that have been raised in the original HGS; (1) avoiding the local search, (2) solving the problem of premature convergence, and (3) balancing between the exploitation and exploration phases. The mHGS has been evaluated by using the IEEE Congress on Evolutionary Computation 2020 (CEC'20) for optimization test and ten medical and chemical datasets. The data have dimensions up to 20000 features or more. The results of the proposed algorithm have been compared to a variety of well-known optimization methods, including improved multi-operator differential evolution algorithm (IMODE), gravitational search algorithm, grey wolf optimization, Harris Hawks optimization, whale optimization algorithm, slime mould algorithm and hunger search games search. The experimental results suggest that the proposed mHGS can generate effective search results without increasing the computational cost and improving the convergence speed. It has also improved the SVM classification performance. [ABSTRACT FROM AUTHOR]

The free convection in a divided enclosure is an ongoing attractive research topic due to its occurrence in various thermal applications, which can be utilized as a dual-purpose application to suppress or enhance the heat transfer rate. In this investigation, a vertical enclosure divided by adiabatic louvers is geometrically modified. To this end, circular perforations are made along the louvers. Then, the steady laminar free convection in the modified divided enclosure is experimentally investigated versus design parameters. The design parameters are the Rayleigh number (7 × 103 ≤ Ra ≤ 1.45 × 104), louver's slant angle (0° ≤ φ ≤ 150°), perforation's diameter to louver's width ratio (0.2 ≤ d/w ≤ 0.6) as well as enclosure's aspect ratio (7.8723 ≤ Ar ≤ 9.3671). After that, a mathematical correlation is extracted for the mean Nusselt number (Num) versus the design parameters. In the following, the differential evolution (DE) algorithm and wingsuit flying search (WFS) optimization algorithm are combined in the frame of a novel combinatorial optimization algorithm. The combinatorial algorithm is then employed to optimize the obtained mathematical correlation. It was reported that the maximum heat transfer rate corresponds to the highest level of the Rayleigh number, d/w ratio as well as Ar. Moreover, for the minimum heat transfer rate, the Rayleigh number, d/w ratio as well as Ar, must be at the lowest level. The maximum and minimum heat transfer rates both occur at the same critical value of the louver's angle (φcrit ≈ 90°). [ABSTRACT FROM AUTHOR]

In this paper, a flexible large-scale ensemble-based optimization algorithm is presented for complex optimization problems. According to the no free lunch theorem, no single optimization algorithm demonstrates superior performance across all optimization problems. Therefore, with the animorphic ensemble optimization (AEO) algorithm presented here, a set of algorithms can be used as an ensemble which demonstrate stronger performance across a wider range of optimization problems than any standalone algorithm. AEO is a high-level ensemble designed to handle large ensembles using a well-defined stochastic migration process. The high-level nature of AEO allows for an arbitrary number of diverse standalone algorithms to interface with one another through an island model interface strategy, where various populations change size according to the performance of the algorithm associated with each population. In this study, AEO is demonstrated using ensembles of both evolutionary and swarm algorithms such as differential evolution, particle swarm, gray wolf optimization, moth-flame optimization, and more, and strong performance is observed. Quantitative diagnostics metrics to describe the migration of individuals across populations are also presented and observed with application to some test problems. In the end, AEO demonstrated strong consistent performance across more than 150 benchmark functions of 10–50 dimensions. [ABSTRACT FROM AUTHOR]

This paper represents the execution of a reliable, steady and wideband fractional order digital integrator (FODI) in respect of infinite impulse response (IIR) filter using a recent nature inspired meta-heuristic optimization technique known as mayfly optimization algorithm (MOA). MOA significantly functions stronger against some of the popular algorithms like real-coded genetic algorithm (RGA), particle swarm optimization (PSO), differential evolution (DE), improved particle swarm optimization (IPSO), whale optimization algorithm (WOA) and chaotic oppositional whale optimization algorithm (COWOA) with reference to different magnitude error performances, explanation character accuracy, convergence profile and computational time required to find the optimal solution. In this paper, the authors thoroughly analyze the convergence behavior and magnitude error metrices of the different order FODI's using WOA, COWOA and MOA. MATLAB simulation results also powerfully uphold the magnitude response efficiency and stability of the proposed different order FODI's over some recently published research papers. [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]

Data and text clustering are popular and frequently used in the data mining domain, mainly to deal with big data analytics. The main problem in these techniques is finding the most coherent clusters allocating similar-related objects into one group. In this paper, an improved clustering analysis approach is proposed using an advanced optimization method called AOAOA. The proposed AOAOA method improved the Aquila optimizer (AO) search performance by the operators of the arithmetic optimization algorithms (AOA) and differential evolution (DE) and using a novel transition mechanism. The primary motivation for this modification is that the original optimizer suffers from local optima stagnation and lacks search balance. Thus, the proposed AOAOA overcame these shortcomings by integrating various powerful search strategies and a new update strategy. Experiments are conducted on two parts; eight standard data clustering datasets and ten text documents benchmark datasets to evaluate the performance of the proposed AOAOA method. The proposed method is compared against several well-known optimization algorithms and advanced state-of-the-art methods published in the literature. The data clustering results also showed promising performance for the proposed AOAOA compared to other comparative data clustering methods. Moreover, the results illustrated that the proposed AOAOA can find new best solutions for several different complicated cases as the text document clustering results. The proposed AOAOA got accurate and robust results compared to several state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Multi-objective evolutionary algorithms face many challenges in optimizing mixed-variable multi-objective problems, such as quantization error, low search efficiency of discontinuous discrete variables, and difficulty in coding non-integer discrete variables. To overcome these challenges, this paper proposes a mixed-variable multi-objective evolutionary algorithm based on estimation of distribution algorithm (MVMO-EDA). Compared with traditional multi-objective evolutionary algorithms, MVMO-EDA has the following improvements: (1) instead of crossover and mutation, statistics and sampling are used to generate offspring, which can avoid the quantization error caused by crossover and mutation operations; (2) using index coding for discrete variables to improve the search efficiency; and (3) a scalable histogram probability distribution model and two crowding distance-based diversity maintenance strategies are used to improve the global optimization ability. The performance of the proposed MVMO-EDA is evaluated on the modified ZDT and DTLZ benchmark sets with mixed-variable, and the results show that MVMO-EDA has a competitive performance both in convergence and diversity. [ABSTRACT FROM AUTHOR]

A biomedical application of a novel metaheuristic optimizer is proposed in this paper by constructing an enhanced arithmetic optimization algorithm (AOA). The latter algorithm was constructed using the logarithmic spiral (Ls) search mechanism from the whale optimization algorithm and the greedy selection scheme from the differential evolution algorithm. The proposed algorithm (Ls-AOA) was tested against unimodal and multimodal benchmark functions and demonstrated better capability comparatively using other efficient metaheuristic algorithms reported in the literature. The constructed Ls-AOA algorithm was then proposed to design a proportional-integral-derivative (PID) controller employed in a functional electrical stimulation (FES) system for the first time. The initial statistical and convergence profile assessment showed better performance of the proposed algorithm. The comparative analyses for transient and frequency responses were performed for the PID-controlled FES system using the original AOA, sine–cosine and particle swarm optimization algorithms and the traditional Ziegler-Nichols tuning scheme. Similarly, the FES system tuned with the latter methods was also assessed for disturbance rejection and noise elimination. All the comparative analyses demonstrated that the proposed Ls-AOA has the greater capability for the challenging biomedical FES system. [ABSTRACT FROM AUTHOR]

In recent years, trochoidal milling has become greatly widespread since it provides high productivity and long tool life, especially when cutting difficult-to-machine materials. Numerous studies have dealt with the analytical description and the experimental investigation of the evolution of cutting force, temperature, tool wear, chatter stability, etc. However, most of these analyses are focusing on the conventional circular and cycloid-shape trajectories. Only a few studies address the potential of improving the efficiency by modifying the tool path shape, leaving significant reserves untapped. If the mathematical formulation of the trajectory cannot be described easily, then a sophisticated optimisation algorithm is needed to determine the appropriate tool path shape. To overcome these difficulties at trochoidal milling of straight slots, the present work proposes a new tool path generation algorithm that can maximize the average material removal rate (MRR) while controlling the tool load. In this approach, the tool path is modelled by a B-spline curve whose control points are optimised using a differential evolution algorithm. The developed tool path generation strategy was compared through simulations and experiments with two widely used solutions viz the traditional cycloid-shaped tool path and a modern approach used in computer-aided manufacturing (CAM) systems. The results reveal that the developed method is able to control the tool load and the smoothness of the tool path, and most importantly, also exceeds the existing methods in terms of productivity. [ABSTRACT FROM AUTHOR]

Travelling Salesman Problem (TSP) is an Np-Hard problem, for which various solutions have been offered so far. Using the Harris Hawk Optimization (HHO) algorithm, this paper presented a new method that uses random-key encoding to generate a tour. This method helps maintain the main capabilities of the HHO algorithm, on the one hand, and to take advantage of the capabilities of active mechanisms in the continuous-valued problem space on the other hand. For the exploration phase, the DE/best/2 mutation mechanism employed in the exploitation phase, besides the main strategies in the HHO algorithm, was used. Ten neighborhood search operators are used, four of which are introduced. These operators were intelligently selected using the MCF. The Lin-Kernighan local search mechanism was utilized to improve the proposed algorithm's performance, and the Metropolis acceptance strategy was employed to escape the local optima trap. Besides, 80 datasets were evaluated in TSPLIB to demonstrate the performance and efficiency of the proposed algorithm. The results showed the excellent performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Wave-induced inundation in coastal zones is a serious problem for residents. Accurate prediction of wave run-up height is a complex phenomenon in coastal engineering. In this study, several machine learning (ML) models are developed to simulate wave run-up height. The developed methods are based on optimization techniques employing the group method of data handling (GMDH). The invasive weed optimization (IWO), firefly algorithm (FA), teaching–learning-based optimization (TLBO), harmony search (HS), and differential evolution (DE) meta-heuristic optimization algorithms are embedded with the GMDH to yield better feasible optimization. Preliminary results indicate that the developed ML models are robust tools for modeling the wave run-up height. All ML models' accuracies are higher than empirical relations. The obtained results show that employing heuristic methods enhances the accuracy of the standard GMDH model. As such, the FA, IWO, DE, TLBO, and HS improve the RMSE criterion of the standard GMDH by the rate of 47.5%, 44.7%, 24.1%, 41.1%, and 34.3%, respectively. The GMDH-FA and GMDH-IWO are recommended for applications in coastal engineering. [ABSTRACT FROM AUTHOR]

Grasshopper optimization algorithm (GOA) is a meta-heuristic algorithm for solving optimization problems by modeling the biological habit and social behavior of grasshopper swarms in nature. Compared with other optimization algorithms, GOA still has room to improve its performance on solving complex problems. Therefore, this paper proposes an improved grasshopper optimization algorithm (EMGOA) based on dynamic dual elite learning and sinusoidal mutation. First of all, dynamic elite learning strategy is adopted to improve the influence of elites on the update process, enabling the algorithm to have a faster convergence speed. Then, sinusoidal function is utilized to guide the mutation of the current global optimal individual during each iteration to avoid the algorithm falling into the local optimum and improve the convergence accuracy of the algorithm. In order to investigate the performance of the proposed EMGOA algorithm, experiments are conducted on 26 benchmark functions and CEC2019 in this paper. The experimental results show that the optimization performance of EMGOA is obviously better than GOA, and EMGOA is competitive with six state-of-the-art meta-heuristic optimization algorithms. [ABSTRACT FROM AUTHOR]

In recent years, the hand-based biometric system has received significant attention in identifying a person. In the hand-based biometric, the finger knuckle print plays a vital role in recognizing a person. The main purpose of this research is to propose an effective feature optimization technique for identifying the best feature vectors for finger knuckle print-based authentication. This work presents a novel feature selection algorithm, fitness index selection with differential evolution (FIS_DE), based on K-nearest neighbor (KNN). Initially, the feature extraction is performed using the conventional methods like principal component analysis, linear discriminant analysis, and independent component analysis. Then, evolutionary algorithm is used for feature selection with best vectors. The population-based metaheuristic algorithm DE proposed is used to optimize the KNN classifier. FIS_DE_KNN is compared with Euclidean and neural network classifiers to show the improved efficiency of the proposed work. In this research, experimental results of the proposed FIS_DE-KNN improve the classification accuracy with an optimized number of features. [ABSTRACT FROM AUTHOR]

Real-world engineering design problems are widespread in various research disciplines in both industry and industry. Many optimization algorithms have been employed to address these kinds of problems. However, the algorithm's performance substantially reduces with the increase in the scale and difficulty of problems. Various versions of the optimization methods have been proposed to address the engineering design problems in the literature efficiently. In this paper, a comprehensive review of the meta-heuristic optimization methods that have been used to solve engineering design problems is proposed. We use six main keywords in collecting the data (meta-heuristic, optimization, algorithm, engineering, design, and problems). It is worth mentioning that there is no survey or comparative analysis paper on this topic available in the literature to the best of our knowledge. The state-of-the-art methods are presented in detail over several categories, including basic, modified, and hybrid methods. Moreover, we present the results of the state-of-the-art methods in this domain to figure out which version of optimization methods performs better in solving the problems studied. Finally, we provide remarkable future research directions for the potential methods. This work covers the main important topics in the engineering and artificial intelligence domain. It presents a large number of published works in the literature related to the meta-heuristic optimization methods in solving various engineering design problems. Future researches can depend on this review to explore the literature on meta-heuristic optimization methods and engineering design problems. [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]

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]

This study's primary purpose is to improve the original krill herd (KH) optimization algorithm by using chaos theory and propose a novel chaotic krill herd (CKH) optimization algorithm. Fourteen different chaotic map functions have been added to the several steps of the KH and CKH optimization algorithms already existing in the literature to improve their performances. Six different well-known benchmark functions have been used to test the performances of the developed algorithm. The proposed algorithm has better performance to reach the global optimum of the objective function which has many local minimums. The proposed algorithm improved the KH and CKH optimization algorithms' performances which already exist in the literature. Proposed novel CKH has been applied to rubber bushing stiffness optimization which is a real automotive industry problem. Obtained results have been compared with KH, CKH, genetic algorithm (GA), differential evaluation algorithm (DE) and particle swarm optimization (PSO). The proposed algorithm has better performance to reach the global optimum of the objective function. The performance and validity of the algorithm have been proved not only by using six different benchmark functions but also by using finite element analysis of rubber bushing. The study is also a unique optimization activity that uses the KH algorithm to optimize rubber bushing by using nonlinear finite element analysis. [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]

Differential evolution (DE) is recognized as a simplistic yet robust evolutionary algorithm: it has been utilized to tackle different challenging optimization problems in various science and engineering disciplines. DE has some disadvantages, such as premature convergence and slow convergence rate, leading to the worst DE execution arrangement. Two DE variations named adaptive parameter selection-based DE (APSDE) and chaotic map hybridization based on DE and PSO (CMHDE-PSO) have been proposed to tackle the issues mentioned above. The proposed variants contain three unique advantages for APSDE: (1) new population initialization scheme to keep up the decent variety of the population diversity; (2) controlled mutation factor technique followed by adaptive decreasing parameter selection procedure; (3) novel mutation strategy with a specific weighted pattern to determine the mutant vector for the mutation operation. Similarly, for CMHDE-PSO (1) novel distribution called Torus for the selection of initial population located in the search space; (2) new parameter adoption technique based on chaotic circle maps defined by chaos theory; (3) average pattern means of two different mutation strategies; (4) and lastly the hybridization of proposed improved DE with PSO to supports DE escaping local minima. Both APSDE and CMHDE-PSO are compared with several standard non-DE old-fashioned optimization algorithms and various advanced DE variants. We accomplish definite experiments behind the powerful searching technique by applying the APSDE and CMHDE-PSO-based mutation and parameter selection strategy for the function optimization and weight optimization of feed-forward neural networks (FFNN) on real-world data classification problems. For data classification performance evaluation, 10 data sets are utilized from the repository of UCI machine learning. Experimental results showed that APSDE and CMHDE-PSO extensively beat different EAs in all test functions and obtained higher accuracy with the recent state-of-the-art algorithms for weight optimization. [ABSTRACT FROM AUTHOR]

In this manuscript, the gain of the EDFA-Raman hybrid optical amplifier (HOA) is maximized using a popular nature inspired Whale Optimization Algorithm (WOA). The dominating parameters of HOA, length of Raman amplifier and EDFA and its pump powers are optimized to get the maximum possible gain. Further, the performance of WOA is compared by using other metaheuristic techniques such as Real coded Genetic Algorithm, Differential Evolution, Particle Swarm Optimization, to optimize the same model of HOA. WOA proves to be a better optimization technique as it is able to find maximum gain, have a better convergence curve and box plot performance as compared to the other three techniques. Moreover, the gain flatness and noise figure performance of HOA is compared with EDFA and Raman amplifier. [ABSTRACT FROM AUTHOR]

Dragonfly algorithm (DA) is a recently introduced, swarm intelligent algorithm and has proved its worth over real-world optimization problems. The algorithm is very efficient but is computationally expensive, has poor exploration properties, and unbalanced cohesion and alignment operation. In the present work, the concept of mutation operators has been exploited and seven new versions of DA have been proposed. Adaptive parameters, division of generations, improved exploitation phase and linearly decreasing population adaptation are also followed to improve the exploration, convergence and other properties of DA. The proposed algorithms are experimentally tested on CEC 2005 data set, CEC 2015 data set and CEC 2011 real-world benchmark problems and compared with respect to memory-based hybrid dragonfly algorithm (MHDA), hybrid memory-based dragonfly algorithm with differential evolution (DADE), sine–cosine crow search algorithm (SCCSA), success history-based adaptation differential evolution (SHADE), covariance matrix adaptation evolution strategy (CMA-ES), SHADE based on semi-parameter adaptation-based CMA-ES (LSHADE-SPACMA), hybrid particle swarm gravitational search algorithm (PSOGSA), hybrid firefly and particle swarm optimization algorithm (HFPSO) and others. Experimental and statistical results further validate that mutation clock-based DA (MDA) performs better than all other algorithms under comparison. [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]

The constrained optimization problem is widely used in real-world applications and always hard to handle when the objective function is multimodal or the constraints are too stringent. In this manuscript, an improved differential evolution algorithm is proposed by using a novel individual evaluation scheme as well as a designed constraint handling technique. Firstly, the constrained optimization problem is converted into a biobjective optimization model in which all constraints are taken as an integrated function and further optimized just like the original objective. Then, based on the present individuals, both a reference point and a dynamic line are generated. The distances from any individual to the reference point as well as the dynamic line are adopted to evaluate the individual, and used to categorize individuals for evolving into groups. In addition, in order to improve the feasibility of individuals, a novel constraint handling technique is presented by utilizing the locations of some infeasible points. Finally, the proposed algorithm is executed on some recent benchmark functions as well as the system reliability redundancy allocation problems, and the computation results show the effectiveness of these presented techniques. [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]

To obtain the optimal set of features in feature selection problems is the most challenging and prominent problem in machine learning. Very few human-related metaheuristic algorithms were developed and solved this type of problem. It motivated us to check the performance of recently developed gaining–sharing knowledge-based optimization algorithm (GSK), which is based on the concept of gaining and sharing knowledge of humans throughout their lifespan. It depends on two stages: beginners–intermediate gaining and sharing stage and intermediate–experts gaining and sharing stage. In this study, two approaches are proposed to solve feature selection problems: FS-BGSK: a novel binary version of GSK algorithm that relies on these two stages with knowledge factor 1 and FS-pBGSK: a population reduction technique that is employed on BGSK algorithm to enhance the exploration and exploitation quality of FS-BGSK. The proposed approaches are checked on twenty two feature selection benchmark datasets from UCI repository that contains small, medium and large dimensions datasets. The obtained results are compared with seven state-of-the-art metaheuristic algorithms; binary differential evolution, binary particle swarm optimization algorithm, binary bat algorithm, binary grey wolf optimizer, binary ant lion optimizer, binary dragonfly algorithm and binary salp swarm algorithm. It concludes that FS-pBGSK and FS-BGSK outperform other algorithms in terms of accuracy, convergence and robustness in most of the datasets. [ABSTRACT FROM AUTHOR]

The selection of the mutation strategy for differential evolution (DE) algorithm plays an important role in the optimization performance, such as exploration ability, convergence accuracy and convergence speed. To improve these performances, an improved differential evolution algorithm with neighborhood mutation operators and opposition-based learning, namely NBOLDE, is developed in this paper. In the proposed NBOLDE, the new evaluation parameters and weight factors are introduced into the neighborhood model to propose a new neighborhood strategy. On this basis, a new neighborhood mutation strategy based on DE/current-to-best/1, namely DE/neighbor-to-neighbor/1, is designed in order to replace large-scale global mutation by local neighborhood mutation with high search efficiency. Then, a generalized opposition-based learning is employed to optimize the initial population and select the better solution between the current solution and reverse solution in order to approximate global optimal solution, which can amend the convergence direction, accelerate convergence, improve efficiency, enhance the stability and avoid premature convergence. Finally, the proposed NBOLDE is compared with four state-of-the-art DE variants by 12 benchmark functions with low-dimension and high-dimension. The experiment results indicate that the proposed NBOLDE has a faster convergence speed, higher convergence accuracy, and better optimization capabilities in solving high-dimensional complex functions. [ABSTRACT FROM AUTHOR]

Differential privacy has been a common framework that provides an effective method of establishing privacy-guaranteed machine learning. Extensive research work has focused on differential privacy stochastic gradient descent (SGD-DP) and its variants under distributed machine learning to improve training efficiency and protect privacy. However, SGD-DP relies on the premise of convex optimization. In large-scale distributed machine learning, the objective function may be more a non-convex objective function, which not only makes the gradient calculation difficult and easy to fall into local optimization. It's difficult to achieve truly global optimization. To address this issue, we propose a novel differential privacy optimization algorithm based on quantum particle swarm theory that suitable for both convex optimization and non-convex optimization. We further comprehensively apply adaptive contraction–expansion and chaotic search to overcome the premature problem, and provide theoretical analysis in terms of convergence and privacy protection. Also, we verify through experiments that the actual application performance of the algorithm is consistent with the theoretical analysis. [ABSTRACT FROM AUTHOR]

Efficient task scheduling is considered as one of the main critical challenges in cloud computing. Task scheduling is an NP-complete problem, so finding the best solution is challenging, particularly for large task sizes. In the cloud computing environment, several tasks may need to be efficiently scheduled on various virtual machines by minimizing makespan and simultaneously maximizing resource utilization. We present a novel hybrid antlion optimization algorithm with elite-based differential evolution for solving multi-objective task scheduling problems in cloud computing environments. In the proposed method, which we refer to as MALO, the multi-objective nature of the problem derives from the need to simultaneously minimize makespan while maximizing resource utilization. The antlion optimization algorithm was enhanced by utilizing elite-based differential evolution as a local search technique to improve its exploitation ability and to avoid getting trapped in local optima. Two experimental series were conducted on synthetic and real trace datasets using the CloudSim tool kit. The results revealed that MALO outperformed other well-known optimization algorithms. MALO converged faster than the other approaches for larger search spaces, making it suitable for large scheduling problems. Finally, the results were analyzed using statistical t-tests, which showed that MALO obtained a significant improvement in the results. [ABSTRACT FROM AUTHOR]

Most production scheduling problems, including standard flexible job shop scheduling problems, assume that machines are continuously available. However, in most cases, due to preventive maintenance activities, machines may not be available for a certain time. Meanwhile, in the entire workshop production process, the transportation process of workpieces cannot be ignored. Therefore, the impact of transportation on the production planning should be considered in the scheduling process. To consider both preventive maintenance and transportation processes in the flexible job shop scheduling problem, this paper proposes a flexible job shop scheduling problem considering preventive maintenance activities and transportation processes and establishes a multi-objective flexible job shop scheduling model optimizing the total energy consumption and total makespan. Furthermore, a multi-region division sampling strategy-based multi-objective optimization algorithm integrated with a genetic algorithm and a differential evolution algorithm (MDSS-MOGA-DE) is proposed to solve the model. In the proposed algorithm, a multi-region division sampling strategy and two evaluation functions are utilized to improve the diversity of solutions. In addition, this paper combines a genetic operation and a differential operation to further enhance the search ability of the algorithm. The validity of the algorithm is verified by a real case. The computational results reveal that the proposed model and algorithm obtain appropriate results and have the potential to be applied to other similar problems. [ABSTRACT FROM AUTHOR]

Harmony search (HS) is a type of population-based optimization algorithm that is introduced based on the idea of musical instruments being tuned to obtain the best harmony state. Several versions of HS have been presented, with global harmony search (GHS) considered one of the most popular. Although GHS is efficient in solving various optimization problems, a new position updating mechanism has been added to improve its efficiency and help it avoid getting stuck in local minima. The novel algorithm proposed in this paper is called the intersect mutation global harmony search algorithm (IMGHSA), which has been tested and evaluated on a set of well-known benchmark functions. The IMGHSA is compared with several improved variants of the HS algorithm, such as the basic version of harmony search (HS), improved differential harmony search, generalized opposition-based learning with global harmony search , and novel global harmony search. The experimental results show that the proposed IMGHSA performs better than the state-of-the-art HS variants and has a more robust convergence when optimizing objective functions in terms of the solution accuracy and efficiency. [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]

This research study aims to enhance the optimization accuracy of the two recently emerged metaheuristics of whale and sine–cosine optimizers by means of the balanced improvements in intensification and diversification phases of the algorithms provided by cellular automata (CA). Stagnation at the early phases of the iterations, which leads to entrapment in local optimum points in the search space, is one of the inherent drawbacks of the metaheuristic algorithms. As a favorable solution alternative to this problem, different types of cellular topologies are implemented into these two algorithms with a view to ameliorating their search mechanisms. Exploitation of the fertile areas in the search domain is maintained by the interaction between the topological neighbors, whereas the improved exploration is resulted from the smooth diffusion of the available population information among the structured neighbors. Numerical experiments have been carried out to assess the optimization performance of the proposed cellular-based algorithms. Optimization benchmark problems comprised of unimodal and multimodal test functions have been applied and numerical results have been compared with those found by some of the state-of-the-art literature optimizers including particle swarm optimization, differential evolution, artificial cooperative search and differential search. Cellular variants have been outperformed by the base algorithms for multimodal benchmark problems of Levy and Penalized1 functions. Then, the proposed cellular algorithms have been applied to two different parameter identification cases in order to test their efficiencies on real-world optimization problems. Extensive performance evaluations on different parameter optimization cases reveal that incorporating the CA concepts on these algorithms not only improves the optimization accuracy but also provides considerable robustness to acquired solutions. [ABSTRACT FROM AUTHOR]

In this paper, a new population-based stochastic optimization algorithm called Hybrid Self-Adaptive Differential Evolution (HSADE) is proposed. The algorithm addresses unconstrained global optimization problems, exploring and combining the best features of some Differential Evolution (DE), obtaining a good balance between exploration and exploitation. These approaches are important for increasing the accuracy and efficiency of a population-based stochastic algorithm and for adapting the control parameter values during the optimization process. Further, they are crucial for increasing the convergence speed and reducing the risk of search stagnation. To verify the performance of the HSADE, 25 benchmark functions were tested presenting an optimal performance when compared to some state-of-the-art DEs algorithms. Furthermore, an experimental problem in an automotive sector, related to automatic internal combustion engine calibration, was used adjusting 300 decision variables. The HSADE achieved a reliable calibration, reducing the time required to perform approximately 90% when comparing to other 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]

This study addresses the optimum cost design of mechanically stabilized earth (MSE) using geosynthetics. The design process of MSEs is mathematically programmed based on an objective function depending on the length of reinforcements and vertical distance of reinforced layers. Design restrictions control the final design to be valid in terms of constraints. The aim is to explore the efficiency of evolutionary-based algorithms in dealing with MSE optimization problem along with automating the minimum cost design of MSE walls. To this end, three evolutionary algorithms, differential evolution (DE), evolution strategy, and biogeography-based optimization algorithm (BBO), are tackled to solve this problem. Comprehensive computational simulations confirm the impact of different effective parameters variation on the final design. Finally, the BBO algorithm performed the best, while DE recorded the most unsatisfactory results. [ABSTRACT FROM AUTHOR]

Swarm behaviors in nature have inspired the emergence of many heuristic optimization algorithms. They have attracted much attention, particularly for complex problems, owing to their characteristics of high dimensionality, nondifferentiability, and the like. A new heuristic algorithm is proposed in this study inspired by the prey location and communication behaviors of electric fish. Nocturnal electric fish have very poor eyesight and live in muddy, murky water, where visual senses are very limited. Therefore, they rely on their species-specific ability called electrolocation to perceive their environment. The active and passive electrolocation capability of such fish is believed to be a good candidate for balancing local and global search, and hence it is modeled in this study. A new heuristic called electric fish optimization (EFO) is introduced and compared with six well-known heuristics (simulated annealing, SA; vortex search, VS; genetic algorithm, GA; differential evolution, DE; particle swarm optimization, PSO; and artificial bee colony, ABC). In the experiments, 50 basic and 30 complex mathematical functions, 13 clustering problems, and five real-world design problems are used as the benchmark sets. The simulation results indicate that EFO is better than or very competitive with its competitors. [ABSTRACT FROM AUTHOR]

Quantum-inspired heuristic search algorithms have attracted considerable research interest in recent years. However, existing quantum simulation methods are still limited on the basis of particle swarm optimizer. This paper explores the principle of memetic computing to develop a novel memetic quantum evolution algorithm for solving global optimization problem. First, we design a quantum theory-based memetic framework to handle multiple evolutionary operators, in which multiple units of different kinds of algorithmic information are harmoniously combined. Second, we propose the memetic evolutionary operator and the quantum evolutionary operator to complete the balance between the global search and the local search. The memetic evolutionary operator emphasizes meme diffusion by the shuffled process to enhance the global search ability. The quantum evolutionary operator utilizes an adaptive selection mechanism for different potential wells to tackle the local search ability. Furthermore, the Newton's gravity laws-based gravitational center and geometric center as two important components are introduced to improve the diversity of population. These units can be recombined by means of different evolutionary operators that are based on the synergistic coordination between exploitation and exploration. Through extensive experiments on various optimization problems, we demonstrate that the proposed method consistently outperforms 11 state-of-the-art algorithms. [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]

The design method of adaptive infinite impulse response (IIR) filter is a challenging problem. Its design principle is to determine the filter parameters by the iteration process of the adaptive algorithm, which is to obtain an optimal model for unknown plant based on minimizing mean square error (MSE). However, many adaptive algorithms cannot adjust the parameters of IIR filter to the minimum MSE. Therefore, a more efficient adaptive optimization algorithm is required to adjust the parameters of IIR filter. In this paper, we propose a selfish herd optimization algorithm based on chaotic strategy (CSHO) and apply it to solving IIR system identification problem. In CSHO, we add a chaotic search strategy, which is a better local optimization strategy. Its function is to search for better candidate solutions around the global optimal solution, which makes the local search of the algorithm more precise and finds out potential global optimal solutions. We use solving IIR system identification problem to verify the effectiveness of CSHO. Ten typical IIR filter models with the same order and reduced order are selected for experiments. The experimental results of CSHO compare with those of bat algorithm (BA), cellular particle swarm optimization and differential evolution (CPSO-DE), firefly algorithm (FFA), hybrid particle swarm optimization and gravitational search algorithm (HPSO-GSA), improved particle swarm optimization (IPSO) and opposition-based harmony search algorithm (OHS), respectively. The experimental results show that CSHO has better optimization accuracy, convergence speed and stability in solving most of the IIR system identification problems. At the same time, it also obtains better optimization parameters and achieves smaller difference between actual output and expected output in test samples. [ABSTRACT FROM AUTHOR]

This paper presents a new multi-objective discreet learnable evolution model (MODLEM) to address the vehicle routing problem with time windows (VRPTW). Learnable evolution model (LEM) includes a machine learning algorithm, like the decision trees, that can discover the correct directions of the evolution leading to significant improvements in the fitness of the individuals. We incorporate a robust strength Pareto evolutionary algorithm in the LEM presented here to govern the multi-objective property of this approach. A new priority-based encoding scheme for chromosome representation in the LEM as well as corresponding routing scheme is introduced. To improve the quality and the diversity of the initial population, we propose a novel heuristic manner which leads to a good approximation of the Pareto fronts within a reasonable computational time. Moreover, a new heuristic operator is employed in the instantiating process to confront incomplete chromosome formation. Our proposed MODLEM is tested on the problem instances of Solomon's VRPTW benchmark. The performance of this proposed MODLEM for the VRPTW is assessed against the state-of-the-art approaches in terms of both the quality of solutions and the computational time. Experimental results and comparisons indicate the effectiveness and efficiency of our proposed intelligent routing approach. [ABSTRACT FROM AUTHOR]

Differential evolution (DE) is a simple yet powerful evolutionary algorithm for numerical optimization. However, the performance of DE significantly relies on its mutation operator and control parameters (scaling factor and crossover rate). In this paper, we propose a novel DE variant by introducing a series of combined strategies into DE, called CSDE. Specifically, in CSDE, to obtain a proper balance between global exploration ability and local exploitation ability, we adopt two mutation operators with different characteristics to produce the mutant vector, and provide a mechanism based on their own historical success rate to coordinate the two adopted mutation operators. Moreover, we combine a periodic function based on one modulo operation, an individual-independence macro-control function and an individual-dependence function based on individual's fitness value information to adaptively produce scaling factor and crossover rate. To verify the effectiveness of the proposed CSDE, comparison experiments contained seven other state-of-the-art DE variants are tested on a suite of 30 benchmark functions and four real-world problems. The simulation results demonstrate that CSDE achieves the best overall performance among the eight DE variants. [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]

Monarch butterfly optimization algorithm (MBO) has recently been proposed as a robust metaheuristic optimization algorithm for solving numerical global optimization problems. To enhance the performance of MBO algorithm, harmony search (HS) is introduced as a mutation operator during the adjusting operator of MBO. A novel hybrid metaheuristic optimization method, the so-called HMBO, is introduced to find the best solution for the global optimization problems. HMBO combines HS exploration with MBO exploitation, and therefore, it produces potential candidate solutions. The implementation process for enhancing MBO method is also presented. To evaluate the effectiveness of this improvement, fourteen standard benchmark functions are used. The mean and the best performance of these benchmark functions in 20, 50, and 100 dimensions demonstrated that HMBO often performs better than the original MBO and other population-based optimization algorithms such as ACO, BBO, DE, ES, GAPBIL, PSO and SGA. Moreover, the t-test result proved that the performance differences between the enhanced HMBO and the original MBO as well as the other optimization methods are statistically significant. [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]

In the aircraft manufacturing process, the tolerance allocation of complex components has an extremely important impact on the life cycle of the aircraft. With the increasing demand for aircraft assembly performance and reliability, tolerance allocation has become one of the most concerned issues for process designers. However, the traditional cost-tolerance models hardly took into account the effects of multiple alternative manufacturing processes. The global search strategy and convergence of optimization methods of the model were insufficient, and it was difficult to obtain more optimized results. Therefore, this paper proposed a novel cost-tolerance model that considers the impact of multiple alternative manufacturing processes on component manufacturing cost and quality loss. Then, a hybrid optimization algorithm combining Monte Carlo simulation and self-adaptive differential evolution (SADE) was presented to achieve cost minimization while ensuring high assembly accuracy. The Monte Carlo simulation was used to solve the problem of tolerance superposition and provided a proper initial population for SADE, which can accelerate the convergence speed and enhances the robustness of the SADE. Moreover, based on the traditional SADE, a new mutation strategy, which expands the diversity of the population, was proposed in combination with the Lévy distribution. Improved SADE algorithm had a good optimization effect for large space, nonlinear and non-derivable discrete problems. Finally, a case study of aircraft door assembly was illustrated to verify the effectiveness of the proposed model. The experimental results show the advantage of the method in achieving the cost reduction compared with traditional method. [ABSTRACT FROM AUTHOR]

In cooperative cognitive radio networks (CCRNs), resource allocation can be viewed as a multi-objective optimization issue in terms of channel capacity as well as, among numerous others, the transmitted power, and the QoS limitations. Many researchers have been undertaken to overcome individual problems, not multi-objective problems. In this paper, we investigate multi-objective problems, such as energy consumption, queuing problems, priority levels of traffic classes, fairness, throughput, and user quality requirements. We propose a hybrid optimization algorithm for CCRNs (HCCRN), which enhances the resource allocation. The first contribution of this paper is to propose the load balance enhanced particle swarm optimization algorithm for energy-efficient cluster formation, which overcomes queuing problems. In the second contribution, we consider multiple factors as the input of a multi-factor differential evolution optimization algorithm for prioritizing the traffic levels. The third contribution is that the fair routing path is computed by a modified gravitational search algorithm that enhances resource allocation throughput. For testing purpose, the proposed HCCRN algorithm applied to IEEE 802.11 WLANs. Simulation results show that the users achieve required resources via the proposed HCCRN, thus providing energy efficiency, fairness, throughput, and QoS. [ABSTRACT FROM AUTHOR]

In engineering practice, most optimization problems have multiple objectives, which are usually in a form of expensive black-box functions. The multiobjective efficient global optimization (MOEGO) algorithms have been proposed recently to sequentially sample the design space, aiming to seek for optima with a minimum number of sampling points. With the advance in computing resources, it is wise to make optimization parallelizable to shorten the total design cycle further. In this study, two different parallelized multiobjective efficient global optimization algorithms were proposed on the basis of the Kriging modeling technique. With use of the multiobjective expectation improvement, the proposed algorithm is able to balance local exploitation and global exploration. To implement parallel computing, the "Kriging Believer" and "multiple good local optima" strategies were adopted here to develop new sample infill criteria for multiobjective optimization problems. The proposed algorithms were applied to five mathematical benchmark examples first, which demonstrated faster convergence and better accuracy with more uniform distribution of Pareto points, in comparison with the two other conventional algorithms. The best performed "Kriging Believer" strategy approach was then applied to two more sophisticated real-life engineering case studies on the tailor-rolled blank (TRB) structures for crashworthiness design. After optimization, the TRB hat-shaped tube achieved a 3% increase in energy absorption and a 10.7% reduction in mass, and the TRB B-pillar attained a 10.1% reduction in mass and a 12.8% decrease in intrusion, simultaneously. These benchmark and engineering examples demonstrated that the proposed methods are fairly promising for being an effective tool for a range of design problems. [ABSTRACT FROM AUTHOR]