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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]

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

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

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

Compared with fuel-powered vehicles, new energy electric vehicles have less carbon emissions. The application of new energy electric vehicles in logistics transportation is very significant to reduce vehicle-emitted greenhouse gases and improve environmental protection. In the logistics network using electric transport vehicles, the reasonable layout of battery swapping stations not only improves the distribution efficiency but also saves the distribution cost. In this paper, an integer programming problem model was formulated for reasonably locating battery swapping stations, where the total costs (including construction costs, operation and maintenance costs, and travel costs) were minimized. To solve this problem, we proposed a solution strategy based on an immune optimization algorithm, two crossover operators, and variable crossover and mutation rates. The effectiveness of the presented model and the performance of the proposed solution approach were evaluated using a case study. Compared with the commercial software CPLEX, the numerical results showed that the proposed algorithm can provide the optimal solution with less computing time. • A mathematical model is formulated for reasonably locating the battery swapping stations in logistics network. • An improved immune optimization algorithm is proposed to solve the developed model. • The application of the proposed model and algorithm to a real case is studied. [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]

Feature selection is an important data processing method to reduce dimension of the raw datasets while preserving the information as much as possible. In this paper, an enhanced version of Black Widow Optimization Algorithm called SDABWO is proposed to solve the feature selection problem. The Black Widow Optimization Algorithm (BWO) is a new population-based meta-heuristic algorithm inspired by the evolution process of spider population. Three main improvements were included into the BWO to overcome the shortcoming of low accuracy, slow convergence speed and being easy to fall into local optima. Firstly, a novel strategy for selecting spouses by calculating the weight of female spiders and the distance between spiders is proposed. By applying the strategy to the original algorithm, it has faster convergence speed and higher accuracy. The second improvement includes the use of mutation operator of differential evolution at mutation phase of BWO which helps the algorithm escape from the local optima. And then, three key parameters are set to adjust adaptively with the increase of iteration times. To confirm and validate the performance of the improved BWO, other 10 algorithms are used to compared with the SDABWO on 25 benchmark functions. The results show that the proposed algorithm enhances the exploitation ability, improves the convergence speed and is more stable when solving optimization problems. Furthermore, the proposed SDABWO algorithm is employed for feature selection. Twelve standard datasets from UCI repository prove that SDABWO-based method has stronger search ability in the search space of feature selection than the other five popular feature selection methods. These results confirm the capability of the proposed method simultaneously improve the classification accuracy while reducing the dimensions of the original datasets. Therefore, SDABWO-based method was found to be one of the most promising for feature selection problem over other approaches that are currently used in the literature. [ABSTRACT FROM AUTHOR]

In this paper, a new compact algorithm is proposed. Two sinusoidal formulas are used to automatically adjust the crossover rate and the mutation scaling factor in the compact Differential Evolution (cDE) metaheuristic. The proposed algorithm, called Compound Sinusoidal cDE, CScDE, is compared to seven state-of-the-art compact algorithms on the well-known BBOB test-bed, the CEC-2014 test suite for continuous optimisation, as well as five real-world optimisation problems chosen from the CEC-2011 benchmarks. The CScDE algorithm outperformed its competitors for most problem categories and over most dimensions. It is also compared to some well-established population-based metaheuristics. • A compact compound sinusoidal differential evolution, CScDE, is proposed. • CScDE uses sinusoidal formulas to adjust the parameters of compact DE. • Both binomial and exponential crossover are tested. • CScDE is applied on 89 reference problems with several dimensions. • CScDE outperformed state-of-the-art compact algorithms. [ABSTRACT FROM AUTHOR]

The optimal power flow (OPF) problem featured as a non-linear, non-convex, large-scale and constrained, still remains a popular and challenging work in power systems optimization. Although various optimization algorithms have been devoted to solving this problem, they suffer from some weak points such as insufficient accuracy as well as most of them are unconstrained optimization algorithms that result in optimal solutions that violate certain security operational constraints. To this end, this paper presents an adaptive constraint differential evolution (ACDE) algorithm, in which the novelty lies primarily in these three points: i) the crossover rate (CR) sorting mechanism is employed to build the relationship of CR and individual fitness values; ii) reusing successful evolution direction is proposed to guide the individual evolution towards promising regions; iii) an advanced constraint handling technique named superiority of feasible solutions (SF) is introduced to effectively deal with constraints in power systems. In order to verify the performance of the presented approach to the OPF problem, the standard IEEE-30 bus system is selected as the test case, in which six optimization objectives including total fuel cost, total fuel cost considering the valve-point effect, real active power losses, voltage deviation, voltage stability and emission are studied. The experimental results demonstrate that the presented approach can provide the smaller cost (800.41132$/h), reducing by up to 3.76% compared to the MPIO-COSR. In terms of the emission, ACDE emits the least emissions (0.204817ton/h). In addition, the proposed method also obtains the best results on the real active power losses (3.084041 MW) and voltage deviation (0.085636p.u.) when compared with other state-of-the-art methods. • Crossover rate sorting mechanism is used to improve the performance of adaptive differential evolution. • Reusing successful evolution direction strategy is proposed. • Constraints in OPF are effectively handled through superiority of feasible solutions. • The proposed algorithm can be an effective alternative for constraint OPF problems. [ABSTRACT FROM AUTHOR]