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This paper proposes a new numerical procedure, based on an evolutionary optimization algorithm, for the simultaneous generation of the three components of the seismic ground acceleration at sites in the vicinity of recording stations. Different from other studies proposed to simulate only one component of the seismic excitation, in this paper, the three components are generated simultaneously in a single stage. The methodology allows the determination of a train of seismic waves, modeled as Morlet's wavelets, that are next used for the generation of ground acceleration components. The parameters of each wave, i.e., amplitude, frequency, duration, time of arrival, and direction, are determined using an evolutionary optimization algorithm. The seismic wave train is validated by comparison with the recorded accelerograms and respective response spectra. Next, these same seismic waves are used for the simultaneous simulation of the three seismic acceleration time histories at locations on the surface close to the seismic station. The results suggest that the proposed methodology can be used for engineering applications in which the ground motion must be specified at various points, for instance in the case of electric power transmission lines or at bridge supports. Highlights: A complete methodology to simultaneously generate the 3 components of seismic accelerograms is proposed in this work. A train of seismic waves is obtained, via Morlet's wavelets, which allows simulating accelerograms at different points. The results showed that the proposed method is a simple and effective tool that can be used in practical situations. [ABSTRACT FROM AUTHOR]

The recently proposed artificial cooperative search (ACS) algorithm is a population-based iterative evolutionary algorithm (EA) for solving real-valued numerical optimization problems. It uses a rotation-invariant line recombination-based mutation strategy and rule-based crossover operator. However, it performs poorly for problems that include closely-related variables because, in these cases, generating uncorrelated feasible trial solution vectors using stochastic crossover methods is extremely difficult, and its mutation and crossover operators are also less effective. This paper adds a new QR-decomposition-based rotation-invariant search strategy to the ACS algorithm to improve its ability to solve such problems. This new, advanced ACS algorithm, called A+, has only one control parameter, α , and experimental results have shown that its performance does not strongly depend on the initial value of α . This paper also examines A+’s performance for noisy point cloud filtering, which is a complex real-world problem. The results of numerical experiments demonstrate that A+’s performance when solving numerical and real-world problems with closely-related variables is better than those of the comparison algorithms. [ABSTRACT FROM AUTHOR]

According to the slow convergence speed and low searching precision of traditional backtracking search optimization algorithm, this paper proposed an improved algorithm based on cellular automaton and orthogonal experimental design. Firstly, the algorithm intrcxluced orthogonal experimental design method into the crossover operator to obtain representative high-quality offspring individuals. Then based on the neighbor mcxlel of cellular automaton, it carried out the orthogonal crossover operation of multiple parents in the domain for individuals, which was beneficial to improve the mining capacity and search efficiency of the algorithm. Finally, in order to balance the global searching and local searching ability of the algorithm, it introduced the dynamic proportional weight of excellent individuals into the cross-population to select and update them, with adopted a new dynamic variation equation. The simulation experiments selected 12 standard test functions and compared with 6 other well-behaved algorithms, the results show that the improved algorithm has obvious advantages in convergence speed and optimization accuracy. [ABSTRACT FROM AUTHOR]

The backtracking search optimization algorithm (BSA) is one of the recently proposed evolutionary algorithms (EAs) for solving numerical optimization problems. In this study, a nature-inspired modified BSA (called SSBSA) is proposed and investigated to improve the exploitation and convergence performance of BSA. Inspired by the species evolution rule and the simulated annealing principle, this paper proposes two modified strategies through introducing a specified retain mechanism and an acceptance probability into BSA. In SSBSA, the specified previous individuals of historical population (oldP) and their corresponding amplitude control factors (F) are retained according to the fitness feedback for the next iteration, and a new adaptive F that could decrease as the number of iterations increases is redesigned by learning the acceptance probability. SSBSA has two main advantages: (1) The way to retain the specified previous information improves BSA's exploitation capability. (2) This new F adaptively controls the diversity of population which makes convergence faster. Simulation experiments are carried on fourteen constrained benchmarks and engineering design problems to test the performance of SSBSA. To fully evaluate the performance of SSBSA, several comparisons between SSBSA and other well-known algorithms are implemented. The experimental results show that SSBSA improves the performance of BSA and its performance is more competitive than that of the other algorithms. [ABSTRACT FROM AUTHOR]

An accurate mathematical model has a vital role in controlling and synchronization of chaotic systems. But generally in real-world problems, parameters are mixed with mismatches and distortions. This paper proposes two simple but effective estimation methods to detect the unknown parameters of chaotic models. These methods focus on improving the performance of a recently proposed evolutionary algorithm called backtracking search optimization algorithm (BSA). In this research firstly, a new operator to generate initial trial population is proposed. Then a group search ability is provided for the BSA by proposing a shuffled BSA (SBSA). Grouping population into several sets can provide a better exploration of search space, and an independent local search of each group increases exploitation ability of the BSA. Also new proposed operator to generate initial trial population, by providing a deep search, increases considerably the quality of solutions. The superiority of the proposed algorithms is investigated on parameter identification of 10 typical chaotic systems. Practical experiences and nonparametric analysis of obtained results show that both of the proposed ideas to improve performance of original BSA are very effective and robust so that the BSA by aforementioned ideas produces similar and promising results over repeated runs. A considerably better performance of proposed algorithms based on average of objective functions demonstrates that the proposed ideas can evolve robustness and consistence of BSA. A comparison of the proposed algorithms in this study with respect to other algorithms reported in the literature confirms a considerably better performance of proposed algorithms. [ABSTRACT FROM AUTHOR]

In order to realize the automatic selection of rule number in fuzzy space partition in the identification of the Takagi–Sugeno (T–S) fuzzy model, a new fuzzy clustering method is proposed in this paper. With the purpose of searching for the optimal fuzzy cluster number and the corresponding cluster centers simultaneously, a novel improved hybrid backtracking search algorithm (IHBSA) has been proposed by introducing the idea of a hybrid encoding scheme as well as a variable valid length chromosome to this optimization algorithm. With a proper cluster validity index taken as the objective function, IHBSA is capable of partitioning the fuzzy space and identifying premise parameters of the T–S fuzzy model without setting a deterministic value of cluster number as a priori. Through the experimental analysis, it is demonstrated that the proposed method possesses higher approximation accuracy with relatively fewer rule number in comparison with traditional ones. Moreover, the T–S fuzzy model is implemented to the practical data of hydroelectric generating units (HGU), and preponderant trajectory matching performance has also been achieved when utilizing the proposed method. [ABSTRACT FROM AUTHOR]

Nonlinear optimal control (NOC) problem with complex dynamic constraints (CDC) is difficult to compute even with direct method. In this paper, a hybrid two-stage approach integrating an improved backtracking search optimization algorithm (IBSA) with the hp-adaptive Gauss pseudo-spectral methods (hpGPM) is proposed. Firstly, BSA is improved to enhance its convergent speed and the global search ability, by adopting the harmony search strategy and an adaptive amplitude control factor with individual optimum fitness feedback. Then, at the beginning stage of the hybrid search process, an initialization generator is constructed using IBSA to find a near optimum solution. When the change in fitness function approaches to a predefined value which is small enough, the search process is replaced by hpGPM to accelerate the search process and find an accurate solution. By this way, the hybrid algorithm is able to find a global optimum more quickly and accurately. Two NOC problems with CDC are examined using the proposed algorithm, and the corresponding Monte Carlo simulations are conducted. The comparison results show the hybrid algorithm achieves better performance in convergent speed, accuracy and robustness. [ABSTRACT FROM AUTHOR]

The development of a robust and accurate fault diagnosis approach under various system and fault conditions is a research area of great interest. The objective of this paper is to develop a new non-destructive approach for wiring diagnosis based on Time Domain Reflectometry (TDR) in one hand and on Backtracking Search Optimization Algorithm (BSA) in the other hand. Real-world case studies are investigated to demonstrate the effectiveness and robustness of the proposed approach. Simulation results evaluated from experimental data demonstrate that the proposed approach can be used for effective diagnosis of complex wiring networks. [ABSTRACT FROM AUTHOR]

For slow convergence and easiness to trap in local optimum of backtracking search optimization algorithm, this paper presented an improved algorithm. It used Maxwell-Boltzmann distribution to generate mutation scale factor and redesigned the mutation strategy based on Maxwell-Boltzmann distribution, which improved convergence speed effectively. Moreover, it added a selection mechanism to mutation strategy to enhance global search ability, which could avoid premature convergence. Through comparing with mutation strategy of differential evolution and numerical experiments on a suite of standard test functions, the fact shows that the improved algorithm not only has faster convergence speed, but also good global search capability. [ABSTRACT FROM AUTHOR]

This paper establishes a model to deal with the short-term hydrothermal generation scheduling (SHTGS) problem. The problem is composed of three interconnected parts: short-term hydrothermal coordination, thermal unit commitment and economic load dispatch. An efficient hybrid method composed of chaotic backtracking search optimization algorithm and binary charged system search algorithm (CBSA–BCSS) is proposed to solve this problem. In order to analyze the effect of the chaotic map on the performance of the method, three different chaotic maps are adopted to integrate into the proposed method and the corresponding consequences are achieved. Furthermore, efficient heuristic search strategies are adopted to handle with the complicated constraints of the SHTGS system. Finally, a hydrothermal unit commitment system is utilized to verify the feasibility and effectiveness of the proposed method. The results demonstrate the efficiency of the hybrid optimization method and the appropriation of the constraint handling strategies. The comparison of the solutions achieved by different methods shows that the proposed method has higher efficiency in terms of solving SHTGS problem. [ABSTRACT FROM AUTHOR]

Dynamic economic dispatch with valve-point effect (DED_vpe) is a dynamic nonlinear high-dimensional optimization problem with non-smooth and non-convex characteristics. Meta-heuristic methods have become the mainstream for solving the DED_vpe problem. However, most of these methods only focus on minimizing the generation costs and ignore the algorithmic robustness. In this paper, an adaptive backtracking search optimization algorithm with a dual-learning strategy (DABSA) is proposed for solving the DED_vpe problem. In DABSA, a dual-learning strategy (DL) based on the current and historical optimal individuals is developed to update each individual. This updating strategy helps DABSA improve solution accuracy and overcome premature convergence. In addition, an adaptive parameter control mechanism (APC), which can automatically adjust parameter 'mixrate' value according to the current iteration number, is presented. To handle the system constraints, a 'repair + penalty' constraints-handling approach is employed to lead non-feasible solutions towards the feasible region quickly. The performance of DABSA is assessed by testing on four DED problems containing 5, 10 and 30 units. The experimental results show that DABSA is very competitive compared with reported representative methods in yielding low fuel costs along with high robustness. • A new method DABSA is proposed for solving DED problem with valve-point effects. • The core innovation of DABSA lies in the design of a dual learning strategy (DL). • BSA is the first attempt in solving DED problem with valve-point effects. • Results on 5, 10 and 30 units show that DABSA is competitive in terms of solution accuracy and robustness. [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]