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This paper proposes a novel multi-hybrid algorithm named DHPN, using the best-known properties of dwarf mongoose algorithm (DMA), honey badger algorithm (HBA), prairie dog optimizer (PDO), cuckoo search (CS), grey wolf optimizer (GWO) and naked mole rat algorithm (NMRA). It follows an iterative division for extensive exploration and incorporates major parametric enhancements for improved exploitation operation. To counter the local optima problems, a stagnation phase using CS and GWO is added. Six new inertia weight operators have been analyzed to adapt algorithmic parameters, and the best combination of these parameters has been found. An analysis of the suitability of DHPN towards population variations and higher dimensions has been performed. For performance evaluation, the CEC 2005 and CEC 2019 benchmark data sets have been used. A comparison has been performed with differential evolution with active archive (JADE), self-adaptive DE (SaDE), success history based DE (SHADE), LSHADE-SPACMA, extended GWO (GWO-E), jDE100, and others. The DHPN algorithm is also used to solve the image fusion problem for four fusion quality metrics, namely, edge-based similarity index ( Q A B / F ), sum of correlation difference (SCD), structural similarity index measure (SSIM), and artifact measure ( N A B / F ). The average Q A B / F = 0.765508 , S C D = 1.63185 , S S I M = 0.726317 , and N A B / F = 0.006617 shows the best combination of results obtained by DHPN with respect to the existing algorithms such as DCH, CBF, GTF, JSR and others. Experimental and statistical Wilcoxon's and Friedman's tests show that the proposed DHPN algorithm performs significantly better in comparison to the other algorithms under test. [ABSTRACT FROM AUTHOR]

Reservoir flood control scheduling is a challenging optimization task, particularly due to the complexity of various constraints. This paper proposes an innovative algorithm design approach to address this challenge. Combining the basic walrus optimization algorithm with the adaptive ε-constraint method and introducing the SPM chaotic mapping for population initialization, spiral search strategy, and local enhancement search strategy based on Cauchy mutation and reverse learning significantly enhances the algorithm's optimization performance. On this basis, innovate an adaptive approach ε A New Algorithm for Constraints and Multi Strategy Optimization Improvement (ε-IWOA). To validate the performance of the ε-IWOA algorithm, 24 constrained optimization test functions are used to test its optimization capabilities and effectiveness in solving constrained optimization problems. Experimental results demonstrate that the ε-IWOA algorithm exhibits excellent optimization ability and stable performance. Taking the Taolinkou Reservoir, Daheiting Reservoir, and Panjiakou Reservoir in the middle and lower reaches of the Luanhe River Basin as a case study, this paper applies the ε-IWOA algorithm to practical reservoir scheduling problems by constructing a three-reservoir flood control scheduling system with Luanxian as the control point. A comparative analysis is conducted with the ε-WOA, ε-DE and ε-PSO (particle swarm optimization) algorithms.The experimental results indicate that ε-IWOA algorithm performs the best in optimization, with the occupied flood control capacity of the three reservoirs reaching 89.32%, 90.02%, and 80.95%, respectively. The control points in Luan County can reduce the peak by 49%.This provides a practical and effective solution method for reservoir optimization scheduling models. This study offers new ideas and solutions for flood control optimization scheduling of reservoir groups, contributing to the optimization and development of reservoir scheduling work. [ABSTRACT FROM AUTHOR]

To overcome the disadvantages of premature convergence and easy trapping into local optimum solutions, this paper proposes an improved particle swarm optimization algorithm (named NDWPSO algorithm) based on multiple hybrid strategies. Firstly, the elite opposition-based learning method is utilized to initialize the particle position matrix. Secondly, the dynamic inertial weight parameters are given to improve the global search speed in the early iterative phase. Thirdly, a new local optimal jump-out strategy is proposed to overcome the "premature" problem. Finally, the algorithm applies the spiral shrinkage search strategy from the whale optimization algorithm (WOA) and the Differential Evolution (DE) mutation strategy in the later iteration to accelerate the convergence speed. The NDWPSO is further compared with other 8 well-known nature-inspired algorithms (3 PSO variants and 5 other intelligent algorithms) on 23 benchmark test functions and three practical engineering problems. Simulation results prove that the NDWPSO algorithm obtains better results for all 49 sets of data than the other 3 PSO variants. Compared with 5 other intelligent algorithms, the NDWPSO obtains 69.2%, 84.6%, and 84.6% of the best results for the benchmark function ( f 1 - f 13 ) with 3 kinds of dimensional spaces (Dim = 30,50,100) and 80% of the best optimal solutions for 10 fixed-multimodal benchmark functions. Also, the best design solutions are obtained by NDWPSO for all 3 classical practical engineering problems. [ABSTRACT FROM AUTHOR]

To overcome the disadvantages of premature convergence and easy trapping into local optimum solutions, this paper proposes an improved particle swarm optimization algorithm (named NDWPSO algorithm) based on multiple hybrid strategies. Firstly, the elite opposition-based learning method is utilized to initialize the particle position matrix. Secondly, the dynamic inertial weight parameters are given to improve the global search speed in the early iterative phase. Thirdly, a new local optimal jump-out strategy is proposed to overcome the "premature" problem. Finally, the algorithm applies the spiral shrinkage search strategy from the whale optimization algorithm (WOA) and the Differential Evolution (DE) mutation strategy in the later iteration to accelerate the convergence speed. The NDWPSO is further compared with other 8 well-known nature-inspired algorithms (3 PSO variants and 5 other intelligent algorithms) on 23 benchmark test functions and three practical engineering problems. Simulation results prove that the NDWPSO algorithm obtains better results for all 49 sets of data than the other 3 PSO variants. Compared with 5 other intelligent algorithms, the NDWPSO obtains 69.2%, 84.6%, and 84.6% of the best results for the benchmark function ( f 1 - f 13 ) with 3 kinds of dimensional spaces (Dim = 30,50,100) and 80% of the best optimal solutions for 10 fixed-multimodal benchmark functions. Also, the best design solutions are obtained by NDWPSO for all 3 classical practical engineering problems. [ABSTRACT FROM AUTHOR]

Substantial deterioration of surface water quality, mainly caused by human activities and climate change, makes the assessment of water quality a global priority. Thus, in this study, four metaheuristic algorithms, namely the particle swarm optimization (PSO), differential evolution (DE), ant colony optimization algorithm (ACOR), and genetic algorithm (GA), were employed to improve the performance of the adaptive neuro-fuzzy inference system (ANFIS) in the evaluation of surface water total dissolved solids (TDS). Monthly and annual TDS were considered as target variables in the analysis. In order to evaluate and compare the authenticity of the models, an economic factor (convergence time) and statistical indices of the coefficient of determination (R2), Kling Gupta efficiency (KGE), root mean squared error (RMSE), mean absolute error (MAE), and Nash-Sutcliff efficiency (NSE) were utilized. The results revealed that the hybrid methods used in this study could enhance the classical ANFIS performance in the analysis of the monthly and annual TDS of both stations. For more clarification, the models were ranked using the TOPSIS approach by simultaneously applying the effects of statistical parameters, temporal and spatial change factors, and convergence time. This approach significantly facilitated decision-making in ranking models. The ANFIS-ACOR annual model considering discharge had the best performance in the Vanyar Station; Furthermore, the ANFIS-ACOR monthly model ignoring discharge was outstanding in the Gotvand Station. In total, after utilizing two defined and proposed temporal and spatial change factors, the ANFIS-ACOR and ANFIS-DE hybrid models had the best and worst performance in TDS prediction, respectively. [ABSTRACT FROM AUTHOR]

Cleft lip and/or cleft palate (CL/P) are the common birth defects that result when facial structures developing in an unborn baby do not close completely. To design for treatment schedule, some constraints including hospital eligibility constraints, capacity limitations, treatment age limitations, multi-hospital assignment, and multidisciplinary care team assignment should be determined. However, efficient treatment scheduling is difficult owing to the complicated conditions of specific treatment. This paper presents a multi-objective mathematical model of the CL/P patient treatment scheduling problem in order to minimize the makespan, travel distance, and total least preference assignment score. Since the problem is NP-hard, a solution method is developed based on differential evolution (DE) with particular encoding and decoding schemes for solving the CL/P patient treatment scheduling problem. The performance of DE is evaluated and compared the results with those obtained from the modified particle swarm optimization. The results show that DE is capable of finding high-quality solutions with fast convergence. To apply the proposed approach for a case study, the CL/P patient treatment scheduling program is formulated. The program can be a decision support system in helping the administrators to schedule the patients in order to identify a list of selected treatments, assign each operation of patients to the selected hospital, and intelligently identify the period of the selected treatments. [ABSTRACT FROM AUTHOR]

The Whale Optimisation Algorithm (WOA) is a meta-heuristic model inspired by the hunting behaviours of humpback whales. Similar to many other meta-heuristic models, e.g., Particle Swarm Optimisation (PSO), Artificial Bee Colony (ABC), and Ant Colony Optimisation (ACO), the WOA is susceptible to the issues of slow convergence and local optima. In this study, we address these short comings by first proposing an Enhanced WOA (EWOA) model for tackling single-objective optimisation. Specifically, EWOA integrates the WOA and Differential Evolution (DE). DE is a population-based algorithm that generates new candidate solutions by combining the existing ones, employing a simple yet robust formula. This amalgamation aids in generating diverse solutions during the exploration stage by utilising a non-linear coefficient vector, adaptive weight, and sub-population strategies. Furthermore, fast non-dominated sorting and crowding distance techniques from the Non-dominated Sorting Genetic Algorithm II (NSGA-II) are incorporated into EWOA, resulting in a multi-objective EWOA (MOEWOA) model. We evaluate both EWOA and MOEWOA with a broad spectrum of benchmark functions. The results from 51 single-objective optimisation problems indicate the usefulness of EWOA in terms of a fast convergence rate and with increased performance. On the other hand, MOEWOA demonstrates a better convergence rate and an effective balance between convergence and diversity in 12 multi-objective optimisation problems. In addition, MOEWOA successfully solves 21 complex multi-objective constrained mechanical design problems, outperforming other compared algorithms at the 95% confidence level. The empirical outcomes of our study indicate the potential of EWOA and MOEWOA for undertaking complex, real-world optimisation problems. [ABSTRACT FROM AUTHOR]

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

In the real market, the item's demand is substantially affected by the item's selling price and frequency of advertising. This study focuses on an optimal ordering policy followed by advertising, pricing, and preservation policies. The present study incorporates the quantity-dependent ordering cost and time-dependent holding cost. A partial prepayment scheme is developed for inventory purchase decisions. The spoilage impact can be effectively reduced by an optimal investment in refrigeration. The optimal decision policy has been proposed by using three metaheuristic algorithms, namely particle swarm optimization, real-coded genetic algorithm, and differential evolution algorithm. A comparison is made for these metaheuristic schemes based on the numerical illustrations. The parameter sensitivity is performed to get insights of the variability in the indicators of the inventory model. [ABSTRACT FROM AUTHOR]

A differential evolution (DE) algorithm with superior-inferior mutation scheme (SIDE) is proposed to solve global optimization problems over continuous space. Firstly, a superior-inferior mutation scheme is introduced based on the superior individual and inferior individual. Secondly, the dynamic adjustment strategy of exploration factor for balancing superior individual and inferior individual is proposed, which indicates how much weight to place on local exploitation and global exploration. They are integrated to DE in different milestones of optimization to balance exploration and exploitation of the search space and can alleviate the premature convergence. In order to verify the performance of SIDE, a set of numerical experiments on 32 benchmark functions are executed for performance comparison with 8 advanced DE variants and 4 non-DE-based algorithms for 30, 50 and 100 variables. The experimental results indicate that SIDE is much better than compared DE algorithms in terms of optimization quality. Furthermore, SIDE has the best adaptability to high dimensional problems. [ABSTRACT FROM AUTHOR]

Under the retailing environment, shelf space-based competition between items with different brands has drawn much attention from practitioners and researchers, the decision for low-temperature stored items on limited shelf spaces are compounding this challenge. This paper discusses the shelf display space partition assignment decision for low-temperature dairy items with two-phase demand in remaining shelf-life to maximize the profit of the shelf. Two-phase demand is assumed for the first time based on the knowledge that consumers' perception on the quality of the dairy item is unequal to its true shelf-life. Moreover, considering shelf partitions in different heights carry different sale gaining potentials, three profit weights are given to the partitions. Herein, a new research model named as SPSPA of shelf profit space partition assignment for the low-temperature stored dairy items is constructed. To effectively solve the proposed SPSPA, a hybrid firefly particle swarm optimization (HFPSO) is developed combing the superiority of particle swarm optimization (PSO) in coarse search and firefly algorithm (FA) in fine search. Numerical experiments have been performed on small- and large-scale cases to verify the performance of the proposed HFPSO algorithm by comparing with genetic algorithm, differential evolution (DE), PSO, and FA based on which management insights are obtained for the shelf space assignment decision. [ABSTRACT FROM AUTHOR]

Differential evolution (DE) has been applied to solve various optimization problems due to its simplicity and high search efficiency. However, researchers have confirmed that it still has some shortcomings such as premature convergence and slow convergence, especially when dealing with complex optimization problems. To address these concerning issues, this paper proposes a hybrid particle swarm-differential evolution algorithm (HPSDE). Firstly, to enhance the optimization performance, a modified updating scheme named particle-swarm mutation strategy is designed and an improved control parameters adaption is developed. Then, DE/rand-to-rand/1 mutation strategy is adopted to increase the population diversity and enhance the ability of particles escaping away from local optima. To achieve an improved DE variant with rapid convergence and fine stability, a random mutation framework is designed to combine the two mutation strategies mentioned above. To evaluate the efficiency of HPSDE algorithm, four different experiments have been taken on twenty-nine benchmark functions. The numerical results validate that HPSDE has better overall performance than the other competitors. Additionally, HPSDE is successfully applied to solve five typical engineering optimization problems. [ABSTRACT FROM AUTHOR]

Automatic tooth arrangement and path planning play an essential role in computer-aided orthodontic treatment. However, state-of-the-art methods have some shortcomings: low efficiency, excessive cost of displacements or collisions and insufficient accuracy. To address these issues, this paper proposes an innovative orthodontic path planning method based on the improved gray wolf optimization algorithm, which is called OPP-IGWO. First, the tooth model is preprocessed to obtain initial pose in which each tooth is segmented and built up with an oriented bounding box. Next, the target pose of each tooth is determined through the ideal dental arch curve and the optimal jaw principle. Finally, the path from the initial pose to the target pose of each tooth is planned based on IGWO, which is improved mainly from three aspects: (1) The greedy idea is adopted to initialize the gray wolf population based on dental interpolation. (2) The linear convergence factor in the traditional gray wolf optimization algorithm (GWO) is replaced with a nonlinear convergence factor. (3) We propose a position update strategy based on a dynamic weighting approach, which introduces a learning rate for each gray wolf. The experimental results show that our OPP-IGWO method outperforms the state-of-the-art methods. Compared with improved genetic algorithm, multiparticle improved swarm optimization, normal simplified mean particle swarm optimization algorithm, improved artificial bee colony algorithm, chaotic gray wolf optimization algorithm, hybrid gray wolf optimization algorithm and differential evolution algorithm, random walk gray wolf Optimization algorithm and reinforcement learning gray wolf optimization algorithm, the OPP-IGWO has an improvement on performance by 15.46%, 2.24%, 7.18%, 15.99%, 7.67%, 1.01%, 1.42%, 10.23%, respectively. [ABSTRACT FROM AUTHOR]

This paper proposed an ensemble methodology comprising neural networks, modified differential evolution algorithm and nonlinear autoregressive network with exogenous inputs (NARX) (called neuro-evolutionary NARX or NE-NARX model) for time series forecasting. In NE-NARX, the structure is designed by connecting the neural model and NARX model, and the weight value connection is optimized by a modified differential evolution algorithm. The effectiveness of the proposed NE-NARX model is tested on two well-known benchmark datasets, including the Canadian lynx and the Wolf sunspot. The proposed model is compared to other models, including the classical backpropagation algorithm, particle swarm optimization, differential evolution (DE) and DE variants. Additionally, an ARIMA model is employed as the benchmark for evaluating the capacity of the proposed model. And then, NE-NARX model is used for hourly energy consumption prediction through comparison with other machine learning models including gated recurrent units, convolutional neural networks (CNN), long short-term memory (LSTM), a hybrid CNN-LSTM and sequence-to-sequence learning. Results show convincingly the superiority of the proposed NE-NARX model over other models. [ABSTRACT FROM AUTHOR]

This paper extends our previous algorithm, termed as Particle Swarm Optimization with Distributed Acceleration Constants (PSODAC), which has been introduced for mitigating congestion based on rescheduling in a deregulated environment. The proposed variant adopts a sequential hybridization of the PSODAC principle with Differential Evolution, which is a well-known evolutionary algorithm. The variant in this paper is termed as Sequentially Hybridized Differential Evolution with Particle Swarm Optimization (SH-DEPSO). The experimental investigations are carried out in the IEEE 14 bus system in two scenarios namely, single point congestion and multipoint congestion. Firstly, the performance investigation is carried out on mitigating the congestion using cost analysis, stability analysis, complexity analysis, and strategy analysis. Secondly, the characteristics of the algorithm are observed by performing convergence analysis and investigating the quality of the solution dynamics. The studies demonstrate the competing performance of SH-DEPSO over PSODAC and the traditional PSO. [ABSTRACT FROM AUTHOR]

The performance of conceptual catchment runoff models may highly depend on the specific choice of calibration methods made by the user. Particle Swarm Optimization (PSO) and Differential Evolution (DE) are two well-known families of Evolutionary Algorithms that are widely used for calibration of hydrological and environmental models. In the present paper, five DE and five PSO optimization algorithms are compared regarding calibration of two conceptual models, namely the Swedish HBV model (Hydrologiska Byrans Vattenavdelning model) and the French GR4J model (modèle du Génie Rural à 4 paramètres Journalier) of the Kamienna catchment runoff. This catchment is located in the middle part of Poland. The main goal of the study was to find out whether DE or PSO algorithms would be better suited for calibration of conceptual rainfall-runoff models. In general, four out of five DE algorithms perform better than four out of five PSO methods, at least for the calibration data. However, one DE algorithm constantly performs very poorly, while one PSO algorithm is among the best optimizers. Large differences are observed between results obtained for calibration and validation data sets. Differences between optimization algorithms are lower for the GR4J than for the HBV model, probably because GR4J has fewer parameters to optimize than HBV. [ABSTRACT FROM AUTHOR]

The occurrence of nuclear accidents is unpredictable and destructive, and it is necessary to develop a reasonable evacuation plan in advance to evacuate affected people to safe places. Aiming at the optimization of vehicle evacuation path in a nuclear emergency with fuzzy demand, an uncertainty vehicle evacuation model is modeled with the objective of minimizing the cumulative radiation dose, and a fuzzy-improved genetic algorithm (F_IGA) is proposed. To reduce the influence of parameters on the algorithm, the crossover probability is dynamically adjusted according to the fitness value. Step size is also introduced to improve the position update formula of the elephant herding optimization, and an adaptive crossover approach is proposed, which is used to improve the algorithm's local optimization ability. To improve the global search capability of the algorithm, the roulette wheel strategy and large neighborhood search algorithm are introduced to locally adjusted individuals, and a simulated degradation operator is introduced in the mutation operation. Further, to avoid planning paths that exceed vehicle capacity, different vehicle paths are independently adjusted according to the current environment to reduce the radiation harm to people. Finally, public test cases and road network structures are selected to verify the performance of the algorithm. The results demonstrate that F_IGA outperforms the other algorithms in terms of convergence accuracy, and the number of iteration convergence, and it has more obvious advantages with the increase of the evacuation scale. [ABSTRACT FROM AUTHOR]

Nowadays, the need for electrical energy became crucial in the world. The co-generation plants, which simultaneously produce electrical and heat energies, are one of the alternative solutions to supply people and industry with both energies. The present work addresses the cost minimization of the nonconvex combined heat and power dispatch problem (CHPED). The nonconvex operating region is handled using the binary method, and the optimization problem is solved using two nature-inspired algorithms, namely the flower pollination algorithm (FPA) and the differential evolution (DE). Penalty functions are adopted to handle all the operating constraints, units' limits, and demands. The results obtained compare the algorithms and those of the literature. It is observed that the fuel cost obtained by the flower pollination algorithm (FPA) is less than the one obtained by the differential evolution (DE) and the particle swarm optimization (PSO). [ABSTRACT FROM AUTHOR]

The aim of this work is to investigate whether retrieving the model parameters of self-potential (SP) anomalies using a combination of differential evolution (DE) and particle swarm optimization (PSO) is possible. This approach hybridizes DE and PSO in a parallel way. Each algorithm is self-contained and obtains a [premature] solution after a user-defined generation number. This hybrid algorithm (DE/PSO) selects the best individual in DE and PSO populations and carries it to the next iteration. Cooperation of DE and PSO can significantly improve the results. Simulations through noise-free synthetic anomalies show that the DE/PSO hybrid algorithm is successful in providing more accurate solutions than those obtained using each single metaheuristic. The algorithm also speeds up the rate of convergence to get the optimum solution. We implemented the algorithm in R programming environment using available metaheuristics packages. Then, the reliability of the code was investigated using some mathematical test functions having two and higher dimensions (unknowns). The performance of the hybrid to invert SP anomalies was tested by synthetic and field data sets. The true model parameters were well-recovered from synthetic data sets including noise-free and noisy data. In the tests with field data, SP anomalies over a shallow ore deposit in Süleymanköy (Türkiye), a deep ore deposit in Arizona (USA), and multiple sources of graphite deposits in KTB borehole site (Germany) were inverted. Low misfit values between the observed and calculated SP anomalies were obtained during the test studies. [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]

Makespan dominates the manufacturing expenses in bakery production. The high energy consumption of ovens also has a substantial impact, which bakers may overlook. Bakers leave ovens running until the final product is baked, allowing them to consume energy even when not in use. It results in energy waste, increased manufacturing costs, and CO2 emissions. This paper investigates three manufacturing lines from small and medium-sized bakeries to find optimum makespan and ovens' idle time (OIDT). A hybrid no-wait flow shop scheduling model considering the constraints that are most common in bakeries is proposed. To find optimal solutions, non-dominated sorting genetic algorithm (NSGA-II), strength Pareto evolutionary algorithm (SPEA2), generalized differential evolution (GDE3), improved multi-objective particle swarm optimization (OMOPSO), and speed-constrained multi-objective particle swarm optimization (SMPSO) were used. The experimental results show that the shortest makespan does not always imply the lowest OIDT. Even the optimized solutions have up to 231 min of excess OIDT, while the makespan is the shortest. Pareto solutions provide promising trade-offs between makespan and OIDT, with the best-case scenario reducing OIDT by 1348 min while increasing makespan only by 61 min from the minimum possible makespan. NSGA-II outperforms all other algorithms in obtaining a high number of good-quality solutions and a small number of poor-quality solutions, followed by SPEA2 and GDE3. In contrast, OMOPSO and SMPSO deliver the worst solutions, which become pronounced as the problem complexity grows. [ABSTRACT FROM AUTHOR]

The conical pick is the most essential tool of excavation machinery such as roadheaders, continuous miners, and shearers for breaking rock in mining and civil engineering operations. For rock cuttability, however, the geometry of conical picks and mechanical parameters of rocks are the most important factors. This study aims to construct an optimized data-driven predictive model to establish a quantitative correlation between strength of rock, geometry of tool, and cutting action data with the mean cutting force (CF). For this purpose, 157 datasets of 47 different materials including rocks, ores, coals, and artificial rocks with uniaxial compressive strength ( σ c ), tensile strength ( σ t ), cone angle (θ ), attack angle (γ ), cutting depth (d ), and mean CF (MCF) are accumulated from the literature. Then, extreme gradient boosting (XGBoost) model is constructed by fine-tuning hyperparameters using grid search, random search, genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution (DE). Based on performance indices that are calculated for each model, i.e., coefficient of determination ( R 2 ), root mean square error (RMSE), and mean absolute percentage error (MAE) for the best performed model, i.e., DE-XGboost are R 2 = 0.812 , RMSE = 2256.90 N , and MAE = 1313.66 N for training stage and R 2 = 0.875 , RMSE = 2104.86 N , and MAE = 1140.42 N for testing stage, respectively. The findings also suggest that using a metaheuristic algorithm to fine-tune the hyperparameters of the XGBoost model can increase prediction accuracy. In the last step, three model interpretation methods viz., the permutation-based variable importance, H-statistic-based variable interaction, and accumulated local effects are applied to sensitivity analysis of the input parameters to predict MCF, providing key insights to model and researchers. The ALE plot demonstrated a complex non-linear relationship between predictors and the response variable. It is revealed that parameters d and θ have the highest and lowest impact on the MCF, respectively. Finally, the successful implementation of this approach provides a solid platform for future studies and can be an alternative to complicated conventional and theoretical methods. Highlights: A predictive model to relate the strength of rock, geometry of tool, and cutting action data to mean cutting force is established. Extreme gradient boosting method is optimized by meta‑heuristic algorithms. Differential evolution outperformed grid search, random search, genetic algorithm, and particle swarm optimization algorithms in tunning process Applying adaptive sampling with a minimum resampling number of five and fivefold CV with three repetitions to validate the results. Application of model interpretation methods to assess the influence, interaction, and relation of parameters with the mean cutting force response. [ABSTRACT FROM AUTHOR]

This paper proposed an automated optimization method to improve the prediction accuracy of thermal finite element analysis for laser welding. The method used particle swarm optimization (PSO) to find the optimal heat source parameters with the minimum prediction error, and the PSO was improved with a boundary mutation strategy of attenuation reflection. To improve the optimization speed, the effects of model geometry and latent heat on the prediction accuracy and solution speed of thermal analysis were studied. The results showed that the reduced 3/50-length model could increase the solution speed with a speed-up of 137 times and had the same predicted results as the full model. The latent heat could increase the nonlinearity, and thus gave rise to a speed-down of 4 times in solution speed but a decrease of 6.06% in prediction error. Therefore, a reduced 3/50-length model considering the latent heat was used in the optimization. The parameter optimization was first performed in the thermal analysis using a welding speed of 2.4 m/min, and then the optimized optimal parameters were used at other 5 speeds. The results showed that the error function of the parameters was a multi-peak function. In addition, it could be seen that the maximum, minimum, and mean errors of the weld cross sections were 9.47%, 4.36%, and 6.81% respectively, while the errors using the trial and error method were 12.48%, 7.20%, and 9.98% respectively. This indicated that the proposed method had a speed-up of about 6 times and higher prediction accuracy. [ABSTRACT FROM AUTHOR]

System virtualization is a key technology nowadays for reducing energy consumption and investment in server computers. However this environment frequently faces the problem of virtual machine placement (VMP). In order to solve this problem effectively, we have proposed a Dual-Operator-based Constrained many-objective Evolutionary Algorithm, DOCEA/D, which employs a Decomposition-based strategy. The algorithm uses differential evolution (DE) as an evolutionary mechanism and also employs a novel diversity maintenance (DM) technique to avoid trapping in local optima. For the purpose of validating the performance of DOCEA/D, it has been compared here with other contemporary many-objective evolutionary algorithms (MaOEAs) namely MOEA/D, NSGA-III and IBEA on three instances of VMP problem. The results on the different quality indicators clearly demonstrate the superiority of DOCEA/D over the counterpart approaches. As a further proof, we graphically show the superiority of our proposed method over the competing approaches and also demonstrate the statistical significance of our results. [ABSTRACT FROM AUTHOR]

As environmental concerns have grown, the combined economic emission dispatch (CEED) problem has gotten a lot of attention. Both the cost of fuel and the emission pollution caused by it must be kept to a minimum. As a result, this paper presents an innovative hybrid approach (ihPSODE) for solving CEED problems. This hybrid technique incorporated novel differential evolution (nDE) and particle swarm optimization (nPSO). Where nDE introduces an improved mutation and crossover approach (to prevent untimely convergence) as well as nPSO introduces a new acceleration coefficient, inertia weight and position improve equation (to alleviate the stagnation). So as to balance among local and global search ability, after ihPSODE population evaluation, the best half individuals are determined and the rest individuals are discarded. Then, nPSO is used in the current population (to sustain exploration and exploitation) and nDE is employed in the nPSO generated population (to improve convergence accuracy). The competence of the proposed algorithms (ihPSODE, nPSO and nDE) is inspected on 23 unconstrained benchmark function and then solved 3 test system (3-, 6- and 40-unit) of economic load dispatch (ELD) and 3 test system (3-, 10- and 40-unit) of CEED problem. The experiments have denoted that the proposed algorithms show competitive results and significant performances. [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]

This paper presents an approach for the automatic parameter calibration (AC) of a hypoplastic constitutive soil model. The calibration software developed in this work simplifies the parameter calibration, reduces the subjective "human" factor on the calibration result and lowers the entry hurdle for the use of the hypoplastic constitutive model. The performance of the software was demonstrated by comparing automatically calibrated parameter sets for two sands and their related simulations of the underlying experimental data with simulations using two reference parameter sets. The first reference parameter set was calibrated the classical way, "by hand", and the second was calibrated using the AC tool ExCalibre. Two different optimization methods were used, namely the Differential Evolution (DE) and the Particle Swarm Optimization (PSO). The simulations performed with the parameters obtained from the AC agree well with the experimental data and show improvements over the reference parameter sets. With respect to the optimization method, the performance of the DE proved superior to that of the PSO. Various measures of comparison were examined to quantify the discrepancy between experiment and simulation. By repeating 500 calibration runs, the dispersion of parameters was determined and correlations between different parameters of the hypoplastic model were found. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive genetic algorithm is used to conduct an in-depth study and analysis of English text background elimination, and a corresponding model is designed. The curve results after the initial character editorialization are curved and transformed, and the adaptive genetic algorithm is used for the transformation to solve the influence of multiple inflection points of curve images on feature extraction. Then, using the minimum deviation method, the error values of the input characters and the sample set in the spatial coordinate system are calculated, and the deviation values of the angle and the straight line are used to match the characters with the smallest deviation value to match the highest degree. To enhance identification accuracy, a genetic algorithm is used to iterate the feature sets of angles and line segments, and the optimum features are ultimately generated via cross evolution of generations. The character library is then utilized as an input item for average grouping for trials, and the resulting feature sets are placed in a position matrix and compared one by one to the samples in the database. It is found that the improved stroke-structure feature extraction algorithm based on a genetic algorithm can improve the recognition accuracy and better accomplish the recognition task with better results compared to others. Finally, by analyzing the limitations and characteristics of traditional particle swarm optimization algorithm and differential evolution algorithm, and giving full play to the advantages and applicability of different algorithms, a new differential evolution particle swarm algorithm with better performance and more stable performance is proposed. The algorithm is based on the PSO algorithm, and when the population update of the PSO algorithm is stagnant and the search space is limited, the crossover and mutation operations of the DE algorithm are used to perturb the population, increase the diversity of the population, and improve the global optimization ability of the algorithm. The algorithm is tested on a common dataset for text mining to verify the effectiveness and feasibility of the algorithm. [ABSTRACT FROM AUTHOR]

Birds are a kind of environmental indicator organism, which can reflect the changes in the ecological environment and biodiversity, and recognition of birdsongs can further help understand and protect birds and natural environment. Extreme learning machine (ELM) has the advantages of fast learning speed and good generalization ability, which is widely used in classification and recognition problems. Input layer weights and hidden layer thresholds are two key factors affecting ELM performance. As one of swarm intelligence optimization methods, differential evolution (DE) can be used to optimize the parameters of ELM. In order to enhance the diversity, convergence speed and global search ability of the DE population, and improve the accuracy and stability of the classification model, this paper proposes a multi-strategy differential evolution method (M-SDE) to optimize the parameters of the ELM. And the differential MFCC feature parameters, extracted from birdsongs, are applied to build classification models of M-SDE_ELM and an ensemble M-SDE_EnELM with optimized ELM for bird species recognition. In the experiments, the ELM models optimized by the swarm intelligence algorithms PSO and GOA are compared and analyzed by hypothesis tests with the M-SDE_ELM and M-SDE_EnELM. Results show that the M-SDE_ELM and M-SDE_EnELM can achieve a classification accuracy of 86.70% and 89.05% in the classification of nine species of birds respectively, and the recognition effect and stability of the M-SDE_EnELM model outperform other models. [ABSTRACT FROM AUTHOR]

This paper proposes a novel two-nozzle cable-driven parallel robot (CDPR) for 3D printing building construction. This system has two independently moving nozzles mounted on the existing printing head. Thus, the printing time can be reduced dramatically with this system as the travel path of the printing head can be reduced to almost half thanks to those two nozzles that print almost half of the printing contour. To fully take advantage of two nozzle structures effectively, the path of the printing head is optimized to secure the minimum travel length of both the printing head and two nozzles. The smoothness of the optimal path is secured by applying the non-uniform rational B-splines (NURBS). In addition, free vibration of the proposed CDPR printer's structure is analyzed to improve the printing quality and help the control of the proposed CDPR by using a finite element formulation of cables of the proposed robot. From the analysis results, the critical design guide—stiffness of the proposed structure could be raised by increasing the minimum cable tension in the tension distribution optimization algorithm—was obtained as well. [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]

To solve the problems of low identification accuracy and complex identification methods in the Bouc-Wen model of the magnetorheological (MR) damper, a new parameter identification method using the fireworks algorithm (FWA) is proposed. According to the experimental results of the dynamic characteristics of the MR damper and the simulation data of the Bouc-Wen model, the FWA is used to identify the seven parameters of the Bouc-Wen model. On the basis of the relationship between the identification results and the command current, the current-controlled Bouc-Wen model (I-Bouc-Wen model) is constructed and compared with the experimental results under different sinusoidal excitation frequencies. Compared with the genetic algorithm (GA), differential evolution (DE) algorithm, and particle swarm optimization (PSO) algorithm, the FWA has the advantage of faster convergence, shorter calculation time, and higher stability in solving the parameter identification problem of the highly nonlinear hysteretic model. Under three harmonic excitations, the average calculation accuracies of the I-Bouc-Wen model reache 88.64 %, 90.45 %, and 81.28 %, respectively, and the dynamic characteristic curve of the model is in basic agreement with the experimental results. It can be used for the subsequent controller design and simulation research and lay a foundation for applying the parameterized model of the MR damper in vibration reduction control. [ABSTRACT FROM AUTHOR]

This paper evaluates the performance and suitability of four different metaheuristic algorithms for optimal sizing of standalone microgrids in remote area. The studied metaheuristic algorithms are particle swarm optimization, differential evolution, water cycle algorithm and grey wolf optimization. These algorithms are applied to optimize the capacity of diesel generator, fuel tank, solar photovoltaic, wind turbine, and battery energy storage in four different AC-coupled standalone microgrids for a remote area community in South Australia. The objective function is selected as the net present value of electricity over a 20-year lifetime. The optimisation study is conducted based on the real data of annual load consumption, ambient temperature, solar insolation, and wind speed of the site. Capital, replacement, and maintenance costs of components in Australian market are incorporated for the economic analysis. An operating power reserve is maintained based on the static and dynamic reserve concepts. Uncertainty analysis based on 10-year real data of renewable energies and load consumption is conducted. Sensitivity analysis is provided for variations of the battery price and capacity. The performance of the applied algorithms is evaluated by comparing the economic and operational results, as well as the computational time and optimization convergence. It is found that differential evolution algorithm is unreliable for optimal sizing problem of the studied standalone microgrids.. [ABSTRACT FROM AUTHOR]

For accurate estimation of streamflow of a mountainous river basin, a novel hybrid method is developed in this study, where gradient-based optimization (GBO) algorithm is employed to adjust adaptive neuro-fuzzy system's (ANFIS) hyperparameters. Two key mountainous basins in Pakistan, namely, Gilgit and Astore basins, were selected to show the model's effectiveness in predicting monthly streamflows using temperature and antecedent streamflow data. Several benchmark methods for optimizing ANFIS parameters were compared, which includes particle swarm optimization (PSO), genetic algorithm (GA), differential evolution (DE), ant colony optimization (ACO) and grey wolf optimization (GWO). The GBO algorithm enhanced ANFIS prediction accuracy more than the other benchmark methods. ANFIS-GBO improved the prediction accuracy of other benchmark algorithms hybrid ANFIS models by about 28–29.5, 28–26.9, 28.7−40.4, 11.2–20.1 and 10.4–20.9% with respect to root mean square error (RMSE), normalized RMSE, mean absolute error, determination coefficient and Nash Sutcliffe Efficiency in the studied basins, respectively. The ANFIS-GBO model also improved the peak streamflow prediction accuracy of ANFIS-GWO, ANFIS-PSO, ANFIS-ACO, ANFIS-GA and ANFIS-DE by about 4–23.7, 23.1–32.4, 15.4–41.6, 18.9–49.4 and 17.2–52.3%, respectively. The ANFIS-GBO also showed higher strength than the other models in estimating streamflows from nearby station data as input. Performance comparison of GBO based ANFIS hybridized model with standalone ANFIS model showed that GBO successfully enhanced the prediction accuracy of ANFIS model by optimal tuning of its parameters. [ABSTRACT FROM AUTHOR]

The island Cuckoo Search (iCSPM) algorithm is a variation of Cuckoo Search that uses the island model and highly disruptive polynomial mutation to solve optimization problems. This article introduces an improved iCSPM algorithm called iCSPM with elite opposition-based learning and multiple mutation methods (iCSPM2). iCSPM2 has three main characteristics. Firstly, it separates candidate solutions into several islands (sub-populations) and then divides the islands among four improved Cuckoo Search algorithms: Cuckoo Search via Lévy flights, Cuckoo Search with highly disruptive polynomial mutation, Cuckoo Search with Jaya mutation and Cuckoo Search with pitch adjustment mutation. Secondly, it uses elite opposition-based learning to improve its convergence rate and exploration ability. Finally, it makes continuous candidate solutions discrete using the smallest position value method. A set of 15 popular benchmark functions indicate iCSPM2 performs better than iCSPM. However, based on sensitivity analysis of both algorithms, convergence behavior seems sensitive to island model parameters. Further, the single-objective IEEE-CEC 2014 functions were used to evaluate and compare the performance of iCSPM2 to four well-known swarm optimization algorithms: distributed grey wolf optimizer, distributed adaptive differential evolution with linear population size reduction evolution, memory-based hybrid dragonfly algorithm and fireworks algorithm with differential mutation. Experimental and statistical results suggest iCSPM2 has better performance than the four other algorithms. iCSPM2's performance was also shown to be favorable compared to two powerful discrete optimization algorithms (generalized accelerations for insertion-based heuristics and memetic algorithm with novel semi-constructive crossover and mutation operators) using a set of Taillard's benchmark instances for the permutation flow shop scheduling problem. [ABSTRACT FROM AUTHOR]

Precise prediction of water quality parameters plays a significant role in making an early alert of water pollution and making better decisions for the management of water resources. As one of the influential indicative parameters, electrical conductivity (EC) has a crucial role in calculating the proportion of mineralization. In this study, the integration of an adaptive hybrid of differential evolution and particle swarm optimization (A-DEPSO) with adaptive neuro fuzzy inference system (ANFIS) model is adopted for EC prediction. The A-DEPSO method uses unique mutation and crossover processes to correspondingly boost global and local search mechanisms. It also uses a refreshing operator to prevent the solution from being caught inside the local optimal solutions. This study uses A-DEPSO optimizer for ANFIS training phase to eliminate defects and predict accurately the EC water quality parameter every month at the Maroon River in the southwest of Iran. Accordingly, the recorded dataset originated from the Tange-Takab station from 1980 to 2016 was operated to develop the ANFIS-A-DEPSO model. Besides, the wavelet analysis was jointed to the proposed algorithm in which the original time series of EC was disintegrated into the sub-time series through two mother wavelets to boost the prediction certainty. In the following, the comparison between statistical metrics of the standalone ANFIS, least-square support vector machine (LSSVM), multivariate adaptive regression spline (MARS), generalized regression neural network (GRNN), wavelet-LSSVM (WLSSVM), wavelet-MARS (W-MARS), wavelet-ANFIS (W-ANFIS) and wavelet-GRNN (W-GRNN) models was implemented. As a result, it was apparent that not only was the W-ANFIS-A-DEPSO model able to rise remarkably the EC prediction certainty, but W-ANFIS-A-DEPSO (R = 0.988, RMSE = 53.841, and PI = 0.485) also had the edge over other models with Dmey mother in terms of EC prediction. Moreover, the W-ANFIS-A-DEPSO can improve the RMSE compared to the standalone ANFIS-DEPSO model, accounting for 80%. Hence, this model can create a closer approximation of EC value through W-ANFIS-A-DEPSO model, which is likely to act as a promising procedure to simulate the prediction of EC data. [ABSTRACT FROM AUTHOR]

In this paper, we analyze the performance of the Differential Evolution (DE) compared with the Genetic Algorithm (GA) and the Particle Swarm Optimization (PSO) applied to the optimization of the detection thresholds of distributed Ordered Statistics Constant False Alarm Rate (OS-CFAR) and Censored Mean Level Detector (CMLD-CFAR) systems using "OR" and "AND" fusion rules at the fusion center. The global systems' detection performance is analyzed in a Gaussian clutter considering identical and non-identical CFAR detectors. The obtained results showed that the DE optimization technique gives better performance than PSO and GA in both cases, either in identical or non-identical CFAR detectors. [ABSTRACT FROM AUTHOR]

With the purpose of improving the accuracy of the wind power short-term forecasting in an effective way, improved wavelet threshold denoising and principal component analysis (PCA) are applied to denoise and reduce the dimension of the original wind power data, the wind power data are reconstructed, and the quality of the information is effectively improved. Next, with the aim of decreasing the nonstationarity of the wind power signal, the reconstructed signal is decomposed in the frequency domain by ensemble empirical mode decomposition (EEMD). In terms of the problem that the extreme learning machine (ELM) algorithm has low stability and accuracy when the quantity of hidden layer neurons is randomly determined, particle swarm optimization algorithm improved (IPSO) by differential evolution (DE) algorithm is introduced to improve the ELM, which is then used to build prediction models for wind power signals in different frequency domains, respectively, and a lower prediction error is achieved. Then, the Markov chain is employed to construct the dynamic combination prediction model, and the weights of different subsignal prediction models are adaptively determined. Compared with adding the prediction values of different subsignals directly, the higher prediction accuracy is demonstrated by the prediction scheme proposed in this paper. At last, actual wind farm operation data are applied to conduct a simulation experiment, and the superiority of the introduced prediction scheme is verified. [ABSTRACT FROM AUTHOR]

The present study proposes a novel optimization framework of the reservoir operation in terms of optimizing storage benefits while alleviating downstream environmental impacts. We utilized adaptive neuro fuzzy inference system as the habitat model to assess physical habitat loss at downstream of the reservoir. Furthermore, we applied metaheuristic algorithms including genetic algorithm, particle swarm optimization, invasive weed optimization, differential evolution algorithm, shuffled frog leaping algorithm, gravity search algorithm, atom search algorithm and bat algorithm to optimize reservoir operation. The developed objective function minimizes difference between physical habitat suitability in the natural flow and the optimal release in the simulated period. Based on the results in the case study, The Nash–Sutcliffe model efficiency coefficient demonstrated that the proposed ANFIS model is reliable to assess physical habitats. Hence, it could be used in a coupled simulation-optimization framework to optimize environmental flow regime downstream of a reservoir. Moreover, decision-making system indicated that gravity search algorithm is the best solution for optimization in terms of reliability, vulnerability and mean absolute error. The proposed method can be a robust framework to minimize physical habitat loss at downstream of the reservoirs. [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 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]

Many metaheuristic methods have been proposed to solve engineering problems in literature studies. One of these is the Jaya algorithm, a new population-based optimization algorithm that has been suggested in recent years to solve complex and continuous optimization problems. Jaya basically adopts the best solution by avoiding the worst ones. Although this process accelerates the convergence for the solution, it causes concessions in the population and results in inadequate local search capacity. To increase the search capability and exploitation performance of the Jaya algorithm, a new local search procedure—Elite Local Search—has been added to the Jaya algorithm in this study without making any changes in its basic search capability. Thus, an efficient and robust strategy that can overcome continuous optimization problems is presented. This new algorithm created with the elite local search procedure is called JayaL. To demonstrate the performance and accuracy of JayaL, 20 different well-known benchmark functions in the literature were used. In addition to JayaL algorithm, these functions were solved with differential evolution (DE), particle swarm optimization (PSO), artificial bee colony (ABC), dragonfly algorithm (DA), grasshopper optimization algorithm (GOA), atom search optimization (ASO) and Jaya algorithms. The performances of JayaL, DE, PSO, ABC DA, GOA, ASO and Jaya algorithms were compared with each other, and experimental results were supported by convergence graphs. At the same time, JayaL has been applied to constrained real-world engineering problems. According to the analyses, it has been concluded that JayaL algorithm is a robust and efficient method for continuous optimization problems. [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]

The effective adoption of online learning depends on user satisfaction as distance education approaches suffer from a lack of commitment that may lead to failures and dropouts. The adaptive learning literature argues that an alternative to achieve student satisfaction is to treat them individually, delivering the educational content in a personalized manner. In addition, the sequencing of this content—called Adaptive Curriculum Sequencing (ACS)—is important to avoid cognitive overload and disorientation. The search for an optimal sequence from ever-growing databases is an NP-Hard combinatorial optimization problem. Although some approaches have been proposed, it is challenging to assess their contributions due to the lack of benchmark data available. This paper presents a procedure to create synthetic dataset to evaluate ACS approaches and, as a concept proof, analyzes metaheuristics usually used in ACS approaches: Genetic Algorithm, Particle Swarm Optimization (PSO) and Prey–Predator Algorithm using student's learning goals and their extrinsic and intrinsic information. We also propose an approach based on Differential Evolution (DE). The computational experiments include synthetic datasets with a varied amount of learning materials and real-world datasets for comparison. The results show that DE performed better than the other methods when less than 500 learning materials are used while PSO performed better for larger problems. [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]

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]

Based on the algorithm structure, each metaheuristic algorithm may have its pros and cons, which may result in high performance in some problems and low functionality in some others. The idea is to hybridize two or more algorithms to cover each other's weaknesses. In this study, particle swarm optimization (PSO), simulated annealing (SA) and differential evolution (DE) are combined to develop a more powerful search algorithm. First, the temperature concept of SA is applied to balance the exploration/exploitation capability of the hybridized algorithm. Then, the DE's mutation operator is used to improve the exploration capability of the algorithm to escape the local minimums. Next, DE's mutation operator has been modified so that past experiences can be used for smarter mutations. Finally, the PSO particles' tendency to their local optimums or the global optimum, which balances the algorithm's random and greedy search, is affected by the temperature. The temperature influences the algorithm's behavior so that the random search is more significant at the beginning, and the greedy search becomes more important as the temperature is reduced. The results are compared with the basic PSO, SA, DE, cuckoo search (CS), and hybridized CS-PSO algorithm on 20 benchmark problems. The comparison reveals that, in most cases, the new algorithm outperforms others. [ABSTRACT FROM AUTHOR]

In this research, a feasible mechanism is developed to determine the optimum number of bus rapid transit (BRT) stations as well as their respective locations along the service corridor. To accomplish this, a mathematical model is developed and optimized by using three different evolutionary algorithms, namely particle swarm optimization (PSO), genetic algorithm (GA), and differential evolution (DE), and the results are compared. The total cost function is composed of two main costs namely the operator's cost, i.e., related to costs on service provider's end, and the user's cost, i.e., related to costs on commuters' end. A functional numerical example with the commuters' demand is worked out by minimizing the cost function, which demonstrates the applicability of the framework. In our case study, PSO outclassed GA and DE on the basis of convergence rate. Since our work has proved the robustness of PSO as compared to GA and DE, we conducted our sensitivity analysis keeping PSO as our benchmark algorithm to study the influence of various parameters on the optimal cost. The computational experiments reveal that the optimal cost is substantially affected by the variations in the commuters' demand, commuters' walking speed, and value of the users' access and in-vehicle time. On the contrary, the acceleration/deceleration delays at a bus station, bus operating cost, and headway have an inconsiderable impact on the optimal cost. [ABSTRACT FROM AUTHOR]

When designing a well field for efficiently extracting groundwater or petroleum, it is common for designers to rely on computational optimization methods to determine the optimal placement of wells. The goal of these methods is to find a well-field solution that maximizes the value of a defined objective function, and to do so while utilizing the least amount of computational effort. To achieve this, researchers have developed algorithms based on a wide range of heuristics. Within groundwater management, popular methods include particle swarm optimization (PSO) and genetic algorithms such as differential evolution (DE). This study seeks to investigate a recently developed method called Extremal Optimization for Well Placement Problems (EO-WPP), and to compare its performance with established methods like PSO and DE. EO-WPP is an optimization method based on the extremal optimization (EO) algorithm. EO optimizes by iteratively identifying and modifying the least effective components of a solution. By following this heuristic, the EO algorithm has the potential to quickly find optimal solutions while requiring minimal computational effort. To test this, the performance of DE, PSO and EO-WPP was compared on four benchmark problems. Two of these are the Rastrigin and the Rosenbrock benchmark functions. These functions were used because of their quick evaluation and their popularity in optimization literature. The third benchmark is a synthetic groundwater model, built to test the methods under the context of groundwater management. The final benchmark is a field problem using the Aberdeen groundwater model in South Dakota. The results reveal that EO-WPP was able to outperform DE and PSO on all tested benchmarks. EO-WPP is an effective and efficient optimization tool for well placement design in groundwater management. [ABSTRACT FROM AUTHOR]