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Nowadays, many algorithms are invented with different strengths and weaknesses, none of which is the best for all cases. Herein, a hybrid optimization algorithm entitled the dynamic hybrid optimization algorithm (DHOA) is presented. We cover the weaknesses of one algorithm with the strengths of another algorithm using a new method of combination. There are two methods for combining algorithms: parallel and sequential. We adopted the parallel method and optimized the algorithm’s performance. In this method, unlike other parallel methods, the population size of the better algorithm is enhanced. Three algorithms were selected due to their relatively different performance in the optimization, so that the results could be more accurately examined. We aimed to achieve better and more accurate results in a shorter time by using the exploitation ability of PSO, HHO, and the crossover of GA. Twenty-three well-known examples were provided to determine the fitness of the proposed method and to compare it with these three algorithms. A group of 10 modern benchmark test functions of Congress on Evolutionary Computation (CEC) was used as an extra evaluation for DHOA. Three well-known engineering examples (10-bar truss, welded beam, and pressure vessel designs) were also examined to evaluate the performance of the proposed method. The three algorithms were the Genetic Algorithm (GA), particle swarm optimization (PSO), and Harris Hawks algorithm (HHO). According to the findings, the proposed method has a faster convergence and better performance than the other algorithms. It also yields better results than its basic algorithms. The Friedman mean rank of the proposed dynamic hybrid optimization was one of the top three algorithms among 23 well-known functions and CEC2019 examples. As for the three famous engineering examples (10-bar truss, welded beam, and pressure vessel designs), it was one of the top three algorithms.

This paper presents two hybrid optimization methods known as PSOHHO and DPSOHHO optimization algorithms. In the first method, using a number of formulae, the top populations are exchanged between the two algorithms and a new population is created and in the second method, we adopted the parallel optimization and optimized its performance. In this method, unlike other parallel methods, the population does not remain constant. With this ability, the strengths of an algorithm can be used to compensate for the weaknesses of the other algorithm. In these methods, no changes are made to the algorithms. The main goal is to use existing algorithms. These methods attain the optimal solution in the shortest time possible. Two algorithms of particleswarm optimization (PSO) and Harris Hawks's optimization (HHO) are used to present this method and two truss samples and CEC209 are considered to confirm the performance of this method. Based on the results, these methods have rapid convergence speed and acceptable results compared to other methods. KEYWORDS: Meta-heuristic algorithms, Hybrid algorithm, Optimization, Dynamic hybrid algorithm, Truss.

In this paper, the importance of voltage stability is explained, which is a great problem in the EPS. The estimation of VS is made a priority so as to make the power system stable and prevent it from reaching voltage collapse. The power transfer stability index (PTSI) is used as a predictor utilized in a PSN to detect the instability of voltages on weakened buses. A PSI is used to obtain a voltage assessment of the PSNs. Two hybrid algorithms are developed. The (CA-NN) and the (PSO-NN). After developing algorithms, they are compared with the actual values of PTSI NR method. The algorithms installed on the 24 bus Iraqi PS. The actual values of PTSI are the targets needed. They are obtained from the NR algorithm when the input data is Vi, δi, Pd, Qd for the algorithm. The results indicate that a weak bus that approaches voltage collapse and all results were approximately the same. There is a slight difference with the actual results and demonstrated classical methods are slower and less accurate than the hybrid algorithms. It also demonstrates the validation and effectiveness of algorithms (CA-NN, and PSO-NN) for assessing voltage-prioritizing algorithms (CA-NN). The MATLAB utilized to obtain most of the results.

Solar power and photovoltaic (PV) systems have become crucial components of the world’s energy portfolio. The PV systems may be engineered in a number of ways, including off-grid, on-grid, and tracking. Incorporating PV systems with traditional sources of power like diesel generators (DGs) or other renewable sources, like windmills, is possible. In this situation, developers, investigators, and experts are striving to create the best design that accommodates the load demand in regards to technological, financial, ecological, and social aspects. To assist in figuring out the best PV size and design, numerous tools, models, and heuristics were created and rolled out. The majority of the tools, models, and techniques used to build PV systems over the past 70 years were described, assessed, and evaluated in this article. It was observed that methods for optimising PV system designs evolved with time and demand. Tool design is often divided into segments such as artificial and classical, solo and hybrid approaches, and others. Hybrid approaches, nevertheless, gained prominence to become the most popular approach because of its adaptability and capacity for handling challenging issues. This paper’s evaluation also helps the readers choose a PV system design tool (approximately 46) that is suited for their needs.

Currently, variational quantum classification algorithms (VQCAs) generally rely on traditional optimization techniques such as Powell and SLSQP in the parameter optimization session. However, the performance of these methods shows limitations in practical applications. Although the parameter-shift rule can efficiently compute the parameter gradient with quantum circuits, it needs to run the quantum circuit twice repeatedly, which significantly reduces the computation efficiency. In order to overcome this challenge, this paper innovatively integrates the principle of unitary operation in quantum mechanics with the technical characteristics of superconducting quantum chips and elaborately designs some new parameterized quantum gates (PQGs). These PQGs strictly follow the rules of unitary operation, which ensures the stability and accuracy of quantum state evolution while realizing an efficient solution to the parameter gradient. Especially for the gradient calculation of a single qubit and single-angle PQGs, the new method can be completed with only a single quantum circuit run, which greatly improves the computation efficiency. Experimental validation on benchmark datasets such as breast cancer and iris shows that the method proposed in this paper exhibits excellent performance on quantum classification tasks. Compared with the parameter-shift rule, the computation efficiency of the new method is improved by 40%. And the classification accuracy, precision, and other key performance metrics are improved by an average of 5% in comparison with traditional optimization algorithms. This work not only enriches the methodology of quantum machine learning theoretically but also demonstrates its remarkable superiority in practical applications, which indicates that the method has great potential in scientific research and industrial applications.

This paper proposes a hybrid estimation algorithm for independently estimating the response function for the center and the response function for the spread in fuzzy regression model. The proposed algorithm combines the least absolute deviations estimation with discriminant analysis. In addition, the F-transform is used to convert spreads of the dependent variable into several groups. Two examples show that our method is superior to the existing methods based on the fuzzy regression model that assumes the same function for spread and center.

This paper presents the comparison of two hybrid methodologies for the two-objective (cost and resilience) design of water distribution systems. The first method is a low-level hybrid algorithm (LLHA), in which a main controller (the non-dominated sorting genetic algorithm II, NSGA-II) coordinates various subordinate algorithms. The second method is a high-level hybrid algorithm (HLHA), in which various sub-algorithms collaborate in parallel. Applications to four case studies of increasing complexity enable the performances of the hybrid algorithms to be compared with each other and with the performance of the NSGA-II. In the case study featuring low/intermediate complexity, the hybrid algorithms (especially the HLHA) successfully capture a more diversified Pareto front, although the NSGA-II shows the best convergence. When network complexity increases, instead, the hybrid algorithms (especially the LLHA) turn out to be superior in terms of both convergence and diversity. With respect to both the HLHA and the NSGA-II, the LLHA is capable of detecting the final front in a single run with a lower computation burden. In contrast, the HLHA and the NSGA-II, which are more affected by the initial random seed, require numerous runs with an attempt to reach the definitive Pareto front. On the other hand, a drawback of the LLHA lies in its reduced ability to deal with general problem formulations, i.e., those not relating to water distribution optimal design.

Self-adaptive variants of evolutionary algorithms (EAs) tune their parameters on the go by learning from the search history. Adaptive differential evolution with optional external archive (JADE) and self-adaptive differential evolution (SaDE) are two well-known self-adaptive versions of differential evolution (DE). They are both unconstrained search and optimization algorithms. However, if some constraint handling techniques (CHTs) are incorporated in their frameworks, then they can be used to solve constrained optimization problems (COPs). In an early work, an ensemble of constraint handling techniques (ECHT) is probabilistically hybridized with the basic version of DE. The ECHT consists of four different CHTs: superiority of feasible solutions, self-adaptive penalty, ε -constraint handling technique and stochastic ranking. This paper employs ECHT in the selection schemes, where offspring competes with their parents for survival to the next generation, of JADE and SaDE. As a result, JADE-ECHT and SaDE-ECHT are developed, which are the constrained variants of JADE and SaDE. Both algorithms are tested on 24 COPs and the experimental results are collected and compared according to algorithms’ evaluation criteria of CEC’06. Their comparison, in terms of feasibility rate (FR) and success rate (SR), shows that SaDE-ECHT surpasses JADE-ECHT in terms of FR, while JADE-ECHT outperforms SaDE-ECHT in terms of SR.

This paper aims to explore torque optimization control issue in the turning of EV (Electric Vehicles) with motorized wheels for reducing energy consumption in this process. A three-degree-of-freedom (3-DOF) vehicle dynamics model is used to analyze the total longitudinal force of the vehicle and explain the influence of torque vectoring distribution (TVD) on turning resistance. The Genetic Algorithm-Particle Swarm Optimization Hybrid Algorithm (GA-PSO) is used to optimize the torque distribution coefficient offline. Then, a torque optimization control strategy for obtaining minimum turning energy consumption online and a torque distribution coefficient (TDC) table in different cornering conditions are proposed, with the consideration of vehicle stability and possible maximum energy-saving contribution. Furthermore, given the operation points of the in-wheel motors, a more accurate TDC table is developed, which includes motor efficiency in the optimization process. Various simulation results showed that the proposed torque optimization control strategy can reduce the energy consumption in cornering by about 4% for constant motor efficiency ideally and 19% when considering the motor efficiency changes in reality.

FAGV is a kind of heavy equipment in the storage environment. Its path needs to be simple and smooth and should be able to avoid sudden obstacles in the process of driving. According to the environmental characteristics of intelligent storage and the task requirements of FAGV, this paper proposed a hybrid dynamic path planning algorithm for FAGV based on improved A* and improved DWA. The improved A* algorithm can plan the global optimal path more suitable for FAGV. The improved evaluation function of DWA can ensure that the local path of FAGV is closer to the global path. DWA combines the rolling window method for local path planning to avoid sudden unknown static and dynamic obstacles. In addition, this paper verifies the effectiveness of the algorithm through simulation. The simulation results show that the algorithm can avoid obstacles dynamically without being far away from the global optimal path.