Search

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]

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]

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]