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This paper presents a novel dynamic Jellyfish Search Algorithm using a Simulated Annealing and disruption operator, called DJSD. The developed DJSD method incorporates the Simulated Annealing operators into the conventional Jellyfish Search Algorithm in the exploration stage, in a competitive manner, to enhance its ability to discover more feasible regions. This combination is performed dynamically using a fluctuating parameter that represents the characteristics of a hammer. The disruption operator is employed in the exploitation stage to boost the diversity of the candidate solutions throughout the optimization operation and avert the local optima problem. A comprehensive set of experiments is conducted using thirty classical benchmark functions to validate the effectiveness of the proposed DJSD method. The results are compared with advanced well-known metaheuristic approaches. The findings illustrated that the developed DJSD method achieved promising results, discovered new search regions, and found new best solutions. In addition, to further validate the performance of DJSD in solving real-world applications, experiments were conducted to tackle the task scheduling problem in cloud computing applications. The real-world application results demonstrated that DJSD is highly competent in dealing with challenging real applications. Moreover, it achieved gained high performances compared to other competitors according to several standard evaluation measures, including fitness function, makespan, and energy consumption. [ABSTRACT FROM AUTHOR]

Holding a lasting balance between the water resources and water demands has become a challenging task for water resources managers, especially in recent years with the looming global warming crisis and its resulting climatic change effects. This paper focuses on modeling the optimized operation of the Zayandehrud Reservoir, located in west-central Iran, under two fifth-generation climate change scenarios called RCP4.5 and RCP8.5. A novel variant of the gravitational search algorithm (GSA), named the adaptive accelerated GSA (AAGSA) is proposed and adopted as the optimizer of the reservoir operation in this paper. The major advancement of the AAGSA against the original GSA is its high exploration capability, allowing the proposal to effectively tackle a variety of difficulties any complex optimization problem can face. The goal of the optimization process is the maximization of the sustainability of supplying the downstream water demands by the reservoir. The optimal results obtained by the original GSA and the proposed AAGSA algorithms suggest that the AAGSA can achieve much more accurate results with much less computational runtime, such that the proposed AAGSA is able to achieve the reservoir operation sustainability index of 98.53% and 99.46%, under RCP4.5 and RCP8.5 scenarios, respectively. These figures are higher than those obtained by the original GSA by 23.5% and 16% under RCP4.5 and RCP8.5, respectively, while the runtime of the proposal is reduced by over 80% in both scenarios, as compared to the GSA, suggesting the high competence of the proposed AAGSA to solve such a high-dimensional and complex real-world engineering problem. [ABSTRACT FROM AUTHOR]

• An improved version of SOS (HCLSOS) is presented to tackle optimization problems. • HCLSOS implements a multiswarm strategy enhanced with comprehensive learning, gaussian scaled commensalism, and a unidirectional information exchange. • The various mechanisms achieved the needed balance between exploration and exploitation. • Standard / CEC2014 / CEC2017 / CEC2022 benchmark functions and constrained engineering problems are tested. • HCLSOS is applied to design an efficient frequency control of a two-area islanded microgrid system • HCLSOS demonstrates superiority to its competing peers in many problems. The Symbiotic Organism Search (SOS) algorithm, and most of its variants, exhibit the same search behaviour. That is, each organism or individual in the ecosystem applies the same update rules according to 3 phases: mutualism, commensalism, and parasitism. By applying this strategy, an organism is capable of crippling the search effort of another organism. To overcome this problem, this paper proposes a Heterogeneous Comprehensive Learning SOS (HCLSOS) which divides the population into two distinct subpopulations namely: the exploration and exploitation subpopulations. HCLSOS allows each organism to follow one of two search behaviour according to into its subpopulation: whether to explore or exploit. This addresses the problem of balancing exploration and exploitation in SOS. The proposed algorithm employs a comprehensive learning strategy for the exploration group to generate a new type of mutual vector called the multispecies mutual vector capable of preserving organisms' diversity and discouraging premature convergence. Information exchange between the two groups is unidirectional and managed through a random elite learning strategy. Through this collaboration, HCLSOS can effectively evolve organisms to explore the search space and properly exploit the discovered optimal regions. The study tested HCLSOS on 23 benchmark functions, CEC2014, CEC2017, and the recent CEC 2022 test suites. The outcome of HCLSOS is compared with 15 state-of-the-art algorithms, and the results obtained showed that HCLSOS can attain competitive or even better results. Furthermore, we applied HCLSOS to solve 3 constrained engineering problems and to design an efficient frequency control of a two-area islanded microgrid system. [ABSTRACT FROM AUTHOR]

• Proposed an alternative method to determine the PEMFC's parameters. • The proposed method depends on using the strengths of new meta heuristic technique named Gorilla Troops Optimizer. • Using a set of real-world datasets to evaluate the performance of developed method. • Compare the results of developed method with well-known PEMFC's parameters. Proton exchange membrane fuel cell (PEMFC) designs include multiple variable quantities with various nonlinear factors, which must be determined precisely to guarantee reliable modeling. The characteristic model of fuel cells (FCs) has an essential role in examining these cells' effective investigation. The FC design substantially influences simulation investigations of such methods, which has emerged in various applications. In this paper, a new identification method for the parameters' of the PEMFC is proposed based on using Gorilla Troops Optimizer (GTO). The optimized fitness function in the proposed method is used by the minimum value of the sum of squared errors (SSEs) of the current and estimated voltage cases. Various test cases are utilized to validate the effectiveness of the proposed method. After carrying many independent runs, the comparative algorithms are analyzed using SSEs, and the standard deviation measures. The high ability of the proposed method is investigated using steady-state and dynamic situations. The results showed that the proposed method got promising results and achieved better performance than other well-known comparative methods. [ABSTRACT FROM AUTHOR]

This paper proposes a novel variant of the Grey Wolf Optimization (GWO) algorithm, named Velocity-Aided Grey Wolf Optimizer (VAGWO). The original GWO lacks a velocity term in its position-updating procedure, and this is the main factor weakening the exploration capability of this algorithm. In VAGWO, this term is carefully set and incorporated into the updating formula of the GWO. Furthermore, both the exploration and exploitation capabilities of the GWO are enhanced in VAGWO via stressing the enlargement of steps that each leading wolf takes towards the others in the early iterations while stressing the reduction in these steps when approaching the later iterations. The VAGWO is compared with a set of popular and newly proposed meta-heuristic optimization algorithms through its implementation on a set of 13 high-dimensional shifted standard benchmark functions as well as 10 complex composition functions derived from the CEC2017 test suite and three engineering problems. The complexity of the proposed algorithm is also evaluated against the original GWO. The results indicate that the VAGWO is a computationally efficient algorithm, generating highly accurate results when employed to optimize high-dimensional and complex problems. [ABSTRACT FROM AUTHOR]

Nowadays, learning-based modeling methods are utilized to build a precise forecast model for renewable power sources. Computational Intelligence (CI) techniques have been recognized as effective methods in generating and optimizing renewable tools. The complexity of this variety of energy depends on its coverage of large sizes of data and parameters, which have to be investigated thoroughly. This paper covered the most resent and important researchers in the domain of renewable problems using the learning-based methods. Various types of Deep Learning (DL) and Machine Learning (ML) algorithms employed in Solar and Wind energy supplies are given. The performance of the given methods in the literature is assessed by a new taxonomy. This paper focus on conducting comprehensive state-of-the-art methods heading to performance evaluation of the given techniques and discusses vital difficulties and possibilities for extensive research. Based on the results, variations in efficiency, robustness, accuracy values, and generalization capability are the most obvious difficulties for using the learning techniques. In the case of the big dataset, the effectiveness of the learning techniques is significantly better than the other computational methods. However, applying and producing hybrid learning techniques with other optimization methods to develop and optimize the construction of the techniques is optionally indicated. In all cases, hybrid learning methods have better achievement than a single method due to the fact that hybrid methods gain the benefit of two or more techniques for providing an accurate forecast. Therefore, it is suggested to utilize hybrid learning techniques in the future to deal with energy generation problems. [ABSTRACT FROM AUTHOR]

Moth–flame optimization (MFO) is a prominent swarm intelligence algorithm that demonstrates sufficient efficiency in tackling various optimization tasks. However, MFO cannot provide competitive results for complex optimization problems. The algorithm sinks into the local optimum due to the rapid dropping of population diversity and poor exploration. Hence, in this article, a migration-based moth–flame optimization (M-MFO) algorithm is proposed to address the mentioned issues. In M-MFO, the main focus is on improving the position of unlucky moths by migrating them stochastically in the early iterations using a random migration (RM) operator, maintaining the solution diversification by storing new qualified solutions separately in a guiding archive, and, finally, exploiting around the positions saved in the guiding archive using a guided migration (GM) operator. The dimensionally aware switch between these two operators guarantees the convergence of the population toward the promising zones. The proposed M-MFO was evaluated on the CEC 2018 benchmark suite on dimension 30 and compared against seven well-known variants of MFO, including LMFO, WCMFO, CMFO, CLSGMFO, LGCMFO, SMFO, and ODSFMFO. Then, the top four latest high-performing variants were considered for the main experiments with different dimensions, 30, 50, and 100. The experimental evaluations proved that the M-MFO provides sufficient exploration ability and population diversity maintenance by employing migration strategy and guiding archive. In addition, the statistical results analyzed by the Friedman test proved that the M-MFO demonstrates competitive performance compared to the contender algorithms used in the experiments. [ABSTRACT FROM AUTHOR]

The optimal power flow (OPF) is a vital tool for optimizing the control parameters of a power system by considering the desired objective functions subject to system constraints. Metaheuristic algorithms have been proven to be well-suited for solving complex optimization problems. The whale optimization algorithm (WOA) is one of the well-regarded metaheuristics that is widely used to solve different optimization problems. Despite the use of WOA in different fields of application as OPF, its effectiveness is decreased as the dimension size of the test system is increased. Therefore, in this paper, an effective whale optimization algorithm for solving optimal power flow problems (EWOA-OPF) is proposed. The main goal of this enhancement is to improve the exploration ability and maintain a proper balance between the exploration and exploitation of the canonical WOA. In the proposed algorithm, the movement strategy of whales is enhanced by introducing two new movement strategies: (1) encircling the prey using Levy motion and (2) searching for prey using Brownian motion that cooperate with canonical bubble-net attacking. To validate the proposed EWOA-OPF algorithm, a comparison among six well-known optimization algorithms is established to solve the OPF problem. All algorithms are used to optimize single- and multi-objective functions of the OPF under the system constraints. Standard IEEE 6-bus, IEEE 14-bus, IEEE 30-bus, and IEEE 118-bus test systems are used to evaluate the proposed EWOA-OPF and comparative algorithms for solving the OPF problem in diverse power system scale sizes. The comparison of results proves that the EWOA-OPF is able to solve single- and multi-objective OPF problems with better solutions than other comparative algorithms. [ABSTRACT FROM AUTHOR]

The moth-flame optimization (MFO) algorithm is an effective nature-inspired algorithm based on the chemical effect of light on moths as an animal with bilateral symmetry. Although it is widely used to solve different optimization problems, its movement strategy affects the convergence and the balance between exploration and exploitation when dealing with complex problems. Since movement strategies significantly affect the performance of algorithms, the use of multi-search strategies can enhance their ability and effectiveness to solve different optimization problems. In this paper, we propose a multi-trial vector-based moth-flame optimization (MTV-MFO) algorithm. In the proposed algorithm, the MFO movement strategy is substituted by the multi-trial vector (MTV) approach to use a combination of different movement strategies, each of which is adjusted to accomplish a particular behavior. The proposed MTV-MFO algorithm uses three different search strategies to enhance the global search ability, maintain the balance between exploration and exploitation, and prevent the original MFO's premature convergence during the optimization process. Furthermore, the MTV-MFO algorithm uses the knowledge of inferior moths preserved in two archives to prevent premature convergence and avoid local optima. The performance of the MTV-MFO algorithm was evaluated using 29 benchmark problems taken from the CEC 2018 competition on real parameter optimization. The gained results were compared with eight metaheuristic algorithms. The comparison of results shows that the MTV-MFO algorithm is able to provide competitive and superior results to the compared algorithms in terms of accuracy and convergence rate. Moreover, a statistical analysis of the MTV-MFO algorithm and other compared algorithms was conducted, and the effectiveness of our proposed algorithm was also demonstrated experimentally. [ABSTRACT FROM AUTHOR]

• A hybrid algorithm combining Harris Hawks Optimizer (HHO) and Moth-Flame Optimization (MFO) is developed. • The Fractional-Order gauss and 2xmod1 Chaotic Maps are employed to generate the initial population. • The operators of MFO are integrated into HHO in order to improve exploration. • Evolutionary Population Dynamics (EPD) is also applied to prevent premature convergence and stagnation in local optima. • The new algorithm is successfully tested in 36 mathematical problems and 4 constrained design problems. This paper proposes a modified version of a contemporary metaheuristic named Harris Hawks Optimizer (HHO) that mimics the foraging strategies used by Harris hawks. It is first argued that exploration ability of HHO is weaker than its exploitation. In addition, the initial position of hawks has the greatest impact on the convergence of the solutions in a similar manner to other metaheuristic algorithms. Then, we applied the Fractional-Order Gauss and 2xmod1 Chaotic Maps to generate the initial population as well as applying the operators of the Moth-Flame Optimization (MFO) to improve the exploration of HHO. In addition, the concept of evolutionary Population Dynamics (EPD) is applied to prevent premature convergence and stagnation in local optima. The method proposed in this work is called FCHMD and evaluated using a set of thirty-six mathematical functions and five engineering problems. The results of the FCHMD are compared with a number of well-known metaheuristics. It can be observed that the FCHMD algorithm outperforms its competitors on the majority of case studies. [ABSTRACT FROM AUTHOR]

This work proposes a new meta-heuristic method called Arithmetic Optimization Algorithm (AOA) that utilizes the distribution behavior of the main arithmetic operators in mathematics including (Multiplication (M), Division (D), Subtraction (S), and Addition (A)). AOA is mathematically modeled and implemented to perform the optimization processes in a wide range of search spaces. The performance of AOA is checked on twenty-nine benchmark functions and several real-world engineering design problems to showcase its applicability. The analysis of performance, convergence behaviors, and the computational complexity of the proposed AOA have been evaluated by different scenarios. Experimental results show that the AOA provides very promising results in solving challenging optimization problems compared with eleven other well-known optimization algorithms. Source codes of AOA are publicly available at and. [ABSTRACT FROM AUTHOR]