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

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

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

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

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

In 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]

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

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

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

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

Driver pathways have been acknowledged to play critical roles in the initiation and progression of cancers►hence it is essential for precision medicine related studies to develop accurate and efficient methods to identify them. Although previous approaches have shown promising results by integrating multi-omics data, their preset artificial parameters may decrease the convenience to use and limit the application scalability. In this paper, a novel integration approach is proposed to incorporate four omics data, i.e., construct a weighted non-binary mutation matrix without presetting artificial parameters. A parameter-free identification model is put forward based on it. It takes advantage of the association between genes in the PPI network as well as balances the contribution of coverage and mutual exclusivity with the harmonic mean. Furthermore, a cooperative co-evolution algorithm is proposed for solving this model. In the algorithm, a particle swarm optimization algorithm suitable for solving combinatorial problems is presented. Three cooperative operators are devised to construct the cooperation among the populations and the swarm to increase the population diversity. Both real biological datasets and simulated ones were exerted to perform experimental comparisons among the proposed method and six other state-of-the-art ones. The gene sets identified by the presented method generally contain more genes involved in known signaling pathways than those obtained by the other methods. Simultaneously, both high accuracy and high efficiency of the proposed method were verified in experiments, making it practical in realistic applications and an effective supplementary tool to identify driver pathways. • A non-binary mutation matrix is constructed without artificial adjustable parameters. • The model incorporates gene association and balances coverage and mutual exclusivity. • A PSO algorithm suitable for solving combinatorial problems is presented. • A co-operative co-evolution algorithm is put forward for solving the presented model. • The performance is compared among the proposed method and seven state of the art ones. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents combination of differential evolution (DE) and biogeography-based optimization (BBO) algorithm to solve complex economic emission load dispatch (EELD) problems of thermal generators of power systems. Emission substances like NO X , SO X , COX, Power demand equality constraint and operating limit constraint are considered here. Differential evolution (DE) is one of the very fast and robust, accurate evolutionary algorithms for global optimization and solution of EELD problems. Biogeography-based optimization (BBO) is another new biogeography inspired algorithm. Biogeography deals with the geographical distribution of different biological species. This algorithm searches for the global optimum mainly through two steps: migration and mutation. In this paper combination of DE and BBO (DE/BBO) is proposed to accelerate the convergence speed of both the algorithm and to improve solution quality. To show the advantages of the proposed algorithm, it has been applied for solving multi-objective EELD problems in a 3-generator system with NO X and SO X emission, in a 6-generators system considering NO X emission, in a 6-generator system addressing both valve-point loading and NO X emission. The current proposal is found better in terms of quality of the compromising and individual solution obtained. [Copyright &y& Elsevier]

Abstract: This paper presents a new approach to the solution of optimal power generation to short-term hydrothermal scheduling problem, using improved particle swarm optimization (IPSO) technique. The practical hydrothermal system is highly complex and possesses nonlinear relationship of the problem variables, cascading nature of hydraulic network, water transport delay and scheduling time linkage that make the problem of finding global optimum difficult using standard optimization methods. In this paper an improved PSO technique is suggested that deals with an inequality constraint treatment mechanism called as dynamic search-space squeezing strategy to accelerate the optimization process and simultaneously, the inherent basics of conventional PSO algorithm is preserved. To show its efficiency and robustness, the proposed IPSO is applied on a multi-reservoir cascaded hydro-electric system having prohibited operating zones and a thermal unit with valve point loading. Numerical results are compared with those obtained by dynamic programming (DP), nonlinear programming (NLP), evolutionary programming (EP) and differential evolution (DE) approaches. The simulation results reveal that the proposed IPSO appears to be the best in terms of convergence speed, solution time and minimum cost when compared with established methods like EP and DE. [Copyright &y& Elsevier]

This paper presents the evolution of combinational logic circuits by a new hybrid algorithm known as the Differential Evolution Particle Swarm Optimization (DEPSO), formulated from the concepts of a modified particle swarm and differential evolution. The particle swarm in the hybrid algorithm is represented by a discrete 3-integer approach. A hybrid multi-objective fitness function is coined to achieve two goals for the evolution of circuits. The first goal is to evolve combinational logic circuits with 100% functionality, called the feasible circuits. The second goal is to minimize the number of logic gates needed to realize the feasible circuits. In addition, the paper presents modifications to enhance performance and robustness of particle swarm and evolutionary techniques for discrete optimization problems. Comparison of the performance of the hybrid algorithm to the conventional Karnaugh map and evolvable hardware techniques such as genetic algorithm, modified particle swarm, and differential evolution are presented on a number of case studies. Results show that feasible circuits are always achieved by the DEPSO algorithm unlike with other algorithms and the percentage of best solutions (minimal logic gates) is higher. [ABSTRACT FROM AUTHOR]

In this paper, a new multi-objective uniform-diversity differential evolution (MUDE) algorithm is proposed and used for Pareto optimum design of mechanisms. The proposed algorithm uses a diversity preserving mechanism called the ε-elimination algorithm to improve the population diversity among the obtained Pareto front. The proposed algorithm is firstly tested on some constrained and unconstrained benchmarks proposed for the special session and competition on multi-objective optimizers held under IEEE CEC 2009. The inverted generational distance (IGD) measure is used to assess the performance of the algorithm. Secondly, the proposed algorithm has been used for multi-objective optimization of two different combinatorial case studies. The first case contains a two-degree of freedom leg mechanism with springs. Three conflicting objective functions that have been considered for Pareto optimization are namely, leg size, vertical actuating force, and the peak crank torque. The second case is a two-finger robot gripper mechanism with two conflicting objectives which are the difference between the maximum and minimum gripping force and the transmission ratio of actuated and experienced gripper forces. Comparisons of obtained Pareto fronts using the method of this work with those obtained in other references show significant improvements. [ABSTRACT FROM AUTHOR]

Dynamic economic dispatch with valve-point effects (DED_ vpe) is a high-dimensional constrained optimization problem with non-convex and non-smooth characteristics. Hybrid methods are one of the most advanced methods to solve the problem. However, most of these methods improve the solution accuracy at the expense of algorithm robustness. This paper proposes an adaptive hybrid backtracking search optimization algorithm (AHBSA) for solving the DED_ vpe. The core idea of AHBSA lies in designing a suitable coupling structure based on the current best individual (called optimal partial coupling). The structure hybridizes an improved BSA mutation operator and the DE/best/1 operator with equal probability. The improved BSA mutation operator uses the current best individual and the historical population to update individual position, called BSA/best/old. It is also the first research work of extending BSA to the problem. In addition, an adaptive parameter control mechanism is proposed to select an appropriate 'mixrate' value for achieving better coupling. The performance of AHBSA is validated on six DED test cases of three systems. Experimental results demonstrate that, compared with some representative methods, AHBSA not only reduces the fuel cost but also ensures the robustness of the algorithm. • A hybrid method AHBSA is proposed to solve DED problem with valve-point effects. • An optimal partial coupling structure is designed in AHBSA. • BSA is the first attempt at solving DED problem. • Results on six benchmark DED test cases show AHBSA is competitive. [ABSTRACT FROM AUTHOR]

Multi-area economic dispatch (MAED) is an important non-linear optimization problem in power system operation. MAED involves multiple power generation areas, and minimizes the total fuel cost by determining the power generation within areas and the power interchange between areas. This paper proposes an improved competitive swarm optimization (ImCSO) algorithm to solve the MAED problems. The ImCSO algorithm introduces two improvements into competitive swarm optimization for performance enhancement. Firstly, a ranking paired learning strategy is adopted to enhance the learning efficiency of the loser particles; secondly, a differential evolution strategy is used to update and improve the winner particles. Combining a constraint repair technique, the proposed ImCSO algorithm is applied to solve 10-unit, 40-unit and 120-unit multi-area static economic dispatch and 40-unit multi-area dynamic economic dispatch problems. The solved MAED problems integrate comprehensive constraints such as valve point effect, multiple fuels, transmission loss, tie-line constraint, prohibited operating zone and ramp rate limit. Through comparison with other well-established optimization algorithms, it is observed that the ImCSO algorithm has superiority in terms of solution accuracy and reliability in solving the MAED problems. • A novel method ImCSO is proposed for solving multi-area economic dispatch (MAED). • Ranking paired learning and differential evolution strategies are adopted in ImCSO. • Static and dynamic MAED problems integrating comprehensive constraints are studied. • Simulation results show that ImCSO has superiority in solution accuracy and reliability. [ABSTRACT FROM AUTHOR]

This paper suggests an algorithm based on Differential Evolution (DE) to calculate Available Transfer Capability (ATC) in the interconnected network. ATC is a measure of unutilized transfer capability of transmission system without violating transmission constraints at a given time and load. The involvement of determination of Total Transfer Capability (TTC), Transmission Reliability Margin (TRM) and Capacity Benefit Margin (CBM) complicates the calculation of ATC. In the present work, TTC is evaluated as an optimization problem using DE technique. TRM is included by introducing single and multi line contingencies in the system. CBM is determined using DE along with Monte Carlo simulation method. The proposed algorithm is applied to modified IEEE 30 and Indian utility 62 bus--three area systems to calculate ATC. A comparison is made in terms of accuracy in the estimation of ATC by DE and Particle Swarm Optimization (PSO). The results show that the margins and methods of incorporating them affect the values of ATC considerably. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we introduce an advanced architecture of K-means clustering-based polynomial Radial Basis Function Neural Networks (p-RBF NNs) designed with the aid of Particle Swarm Optimization (PSO) and Differential Evolution (DE) and develop a comprehensive design methodology supporting their construction. The architecture of the p-RBF NNs comes as a result of a synergistic usage of the evolutionary optimization-driven hybrid tools. The connections (weights) of the proposed p-RBF NNs being of a certain functional character and are realized by considering four types of polynomials. In order to design the optimized p-RBF NNs, a prototype (center value) of each receptive field is determined by running the K-means clustering algorithm and then a prototype and a spread of the corresponding receptive field are further optimized through running Particle Swarm Optimization (PSO) and Differential Evolution (DE). The Weighted Least Square Estimation (WLSE) is used to estimate the coefficients of the polynomials (which serve as functional connections of the network). The performance of the proposed model and the comparative analysis involving models designed with the aid of PSO and DE are presented in case of a nonlinear function and two Machine Learning (ML) datasets [Copyright &y& Elsevier]

A hybrid quantum-behaved particle swarm optimization (QPSO) based on cultural algorithm (CA), which we call cultural QPSO, is proposed. Although QPSO is a promising algorithm for many optimization problems, it is apt to lose the diversity of the swarm in the later period of the search and prematurely converges to the local optimum. Inspired by the structure of human society, this paper uses the CA model to diversify the QPSO population and improve the QPSO's performance. In this model, the swarm is divided into two sub-swarms: the common particle and the elite particle sub-swarm. If a particle comes from a common sub-swarm, it will evolve according to the QPSO method, and during the evolvement, it will be affected not only by the other common particles but also by the elites. For the elites, the differential evolution (DE) method is adopted for evolvement. After each generation, the elites will be re-elected from the whole swarm according to fitness values. The simulation results on benchmark functions demonstrate that cultural QPSO outperforms the original QPSO for many problems. [ABSTRACT FROM AUTHOR]

Wireless sensor networks are appealing, largely because they do not need wired infrastructure, but it is precisely this feature that renders them energy-constrained. The duty cycle scheduling is perceived as a contributor to the energy efficiency of sensing. This paper developed a novel paradigm for modeling wireless sensor networks; in this context, an adaptive sensing scheduling strategy is proposed depending on event occurrence behavior, and the scheduling problem is framed as an optimization problem. The optimization objectives include reducing energy depletion and optimizing detection accuracy. We determine the explicit form of the objective function by numerical fitting and found that the objective function aggregated by the fitting functions is a bivariate multimodal function that favors the Fibonacci tree optimization algorithm. Then, with the optimal parameters optimized by the Fibonacci tree optimization algorithm, the scheduling scheme can be easily deployed, and it behaves consistently in the coming hours. The proposed "Fibonacci Tree Optimization Strategy" ("FTOS") outperforms lightweight deployment-aware scheduling (LDAS), balanced-energy scheduling (BS), distributed self-spreading algorithm (DSS) and probing environment and collaborating adaptive sleeping (PECAS) in achieving the aforementioned scheduling objectives. The Fibonacci tree optimization algorithm has attained a better optimistic effect than the artificial bee colony (ABC) algorithm, differential evolution (DE) algorithm, genetic algorithm (GA) algorithm, particle swarm optimization (PSO) algorithm, and comprehensive learning particle swarm optimization (CLPSO) algorithm in multiple runs. [ABSTRACT FROM AUTHOR]

• A novel enhanced MPA algorithm has been proposed for effective PV modelling. • Static and dynamic PV model parameters have been estimated. • The estimated parameters have been validated via experimental data-sets. • The statistical analysis has been performed for effectiveness of the EMPA method. • The results confirm the EMPA efficiency comparing with state-of-the-art algorithms. Providing an accurate and precise photovoltaic model is a vital stage prior to the system design, therefore, this paper proposes a novel algorithm, enhanced marine predators algorithm (EMPA), to identify the unknown parameters for different photovoltaic (PV) models including the static PV models (single-diode and double-diode) and dynamic PV model. In the proposed EMPA, the differential evolution operator (DE) is incorporated into the original marine predators algorithm (MPA) to achieve stable, and reliable performance while handling that nonlinear optimization problem of PV modeling. Three different real datasets are used to show the effectiveness of the proposed algorithm. In the first case study, the proposed algorithm is used to identify the unknown parameters of a single-diode and double-diode PV models. The root-mean-square error (RMSE) and standard deviation (STD) values for a single-diode are 7.7301 e - 04 and 5.9135 e - 07 . Similarly for double diode are 7.4396 e - 04 and 3.1849 e - 05 , respectively. In addition, the second case study is used to test the proposed model in identifying the unknown parameters of a double-diode PV model. Here, the proposed algorithm is compared with classical MPA in five scenarios at different operating conditions. In this case study, the RMSE and STD of the proposed algorithm are less than that obtained by the MPA algorithm. Moreover, the third case study is utilized to test the ability of the proposed model in identifying the parameters of a dynamic PV model. In this case study, the performance of the proposed algorithm is compared with the one obtained by MAP and heterogeneous comprehensive learning particle swarm optimization (HCLPSO) algorithms in terms of RMSE ± STD. The obtained value of RMSE ± STD by the proposed algorithm is 0.0084505 ± 1.0971 e - 17 , which is too small compared with that obtained by MPA and HCLPSO algorithms (0.0084505 ± 9.6235 e - 14 and 0.0084505 ± 2.5235 e - 9). The results show the proposed model's superiority over the MPA and other recent proposed algorithms in data fitting, convergence rate, stability, and consistency. Therefore, the proposed algorithm can be considered as a fast, feasible, and a reliable optimization algorithm to identify the unknown parameters in static and dynamic PV models. The code of the dynamic PV models is available via this link: https://github.com/DAyousri/Identifying-the-parameters-of-the-integer-and-fractional-order-dynamic-PV-models?_ga=2.104793926.732834951.1616028563-1268395487.1616028563. [ABSTRACT FROM AUTHOR]

In practical applications, solving multimodal multiobjective optimization problems (MMOPs), which have multiple Pareto optimal sets (PSs) in the decision space mapping to the same Pareto front (PF) in the objective space, has great significance for decision makers. The issue of how to maintain diversity in both decision space and objective space remains a key problem for existing multimodal multiobjective evolutionary algorithms. To address this issue, a novel grey prediction evolution algorithm for multimodal multiobjective optimization, termed MMGPE, is proposed in this paper. This is the first time that the grey prediction evolution algorithm (GPE), which is a recently proposed competitive optimization algorithm with strong exploration capability, is improved for MMOPs. These improvements are conducted in the following four aspects: (1) an initialization operator based on particle swarm optimization, (2) an adaptive parameter setting strategy depending on the domain of decision variables of MMOPs, (3) an accelerating convergence mechanism inspired by the niche principle, and (4) an environmental selection operator based on a nondominated sorting mechanism and a special crowding distance approach. The performance of MMGPE is compared with two state-of-the-art multiobjective evolutionary algorithms and four multimodal multiobjective evolutionary algorithms on 11 multimodal multiobjective test functions. MMGPE is also applied to solve a practical problem. The results show the MMGPE's effectiveness and superiority in achieving the goal of finding multiple PSs while obtaining a well-distributed PF. [ABSTRACT FROM AUTHOR]

• Proposing a novel straightforward algorithm for solving various OPF problems. • Introducing an efficient fuzzy strategy to accommodate the inertia coefficient of heuristic algorithm. • Conducting accuracy tests via ten different benchmark problems. • Applying the proposed algorithm on small- and large-scale OPF problems. The optimal power flow (OPF) is a key tool in the planning and operation of power systems, and aims to optimize the operational costs involved in the production and transport of energy by adjusting control variables to meet operational, economic, and environmental constraints. To achieve this goal, a successful implementation of an expeditious and reliable optimization algorithm is crucial. To this end, this paper proposes and scrutinizes a novel fuzzy adaptive hybrid configuration oriented to a joint self-adaptive particle swarm optimization (SPSO) and differential evolution algorithms, namely FAHSPSO-DE, to address the multi-objective OPF (MOOPF) problem. For the sake of practicality, the objectives with innate differences such as total fuel cost, active power losses, and the emission are selected. Due to the practical limitations in real power systems, additional restrictions, including valve-point effect, multi-fuel characteristic, and prohibited operating zones, are also taken into account. In order to validate the performance of the proposed approach, ten various benchmark functions are examined, while three IEEE standard systems such as IEEE 30-, 57-, and 118-bus test systems are employed to demonstrate the performance and suitability of the proposed approach in solving the OPF problem expeditiously. Results have been compared with those in the literature and show the effectiveness of our proposal in handling different scales, multi-objective, and non-convex optimization problems. [ABSTRACT FROM AUTHOR]