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The (1 + (λ , λ)) genetic algorithm is a younger evolutionary algorithm trying to profit also from inferior solutions. Rigorous runtime analyses on unimodal fitness functions showed that it can indeed be faster than classical evolutionary algorithms, though on these simple problems the gains were only moderate. In this work, we conduct the first runtime analysis of this algorithm on a multimodal problem class, the jump functions benchmark. We show that with the right parameters, the (1 + (λ , λ)) GA optimizes any jump function with jump size 2 ≤ k ≤ n / 4 in expected time O (n (k + 1) / 2 e O (k) k - k / 2) , which significantly and already for constant k outperforms standard mutation-based algorithms with their Θ (n k) runtime and standard crossover-based algorithms with their O ~ (n k - 1) runtime guarantee. For the isolated problem of leaving the local optimum of jump functions, we determine provably optimal parameters that lead to a runtime of (n / k) k / 2 e Θ (k) . This suggests some general advice on how to set the parameters of the (1 + (λ , λ)) GA, which might ease the further use of this algorithm. [ABSTRACT FROM AUTHOR]

The flower pollination algorithm (FPA) is a highly efficient metaheuristic optimization algorithm that is inspired by the pollination process of flowering species. FPA is characterised by simplicity in its formulation and high computational performance. Previous studies on FPA assume fixed parameter values based on empirical observations or experimental comparisons of limited scale and scope. In this study, a comprehensive effort is made to identify appropriate values of the FPA parameters that maximize its computational performance. To serve this goal, a simple non-iterative, single-stage sampling tuning method is employed, oriented towards practical applications of FPA. The tuning method is applied to the set of 28 functions specified in IEEE-CEC'13 for real-parameter single-objective optimization problems. It is found that the optimal FPA parameters depend significantly on the objective functions, the problem dimensions and affordable computational cost. Furthermore, it is found that the FPA parameters that minimize mean prediction errors do not always offer the most robust predictions. At the end of this study, recommendations are made for setting the optimal FPA parameters as a function of problem dimensions and affordable computational cost. [ABSTRACT FROM AUTHOR]

Modern scientific applications are composed of various methods, techniques and models to solve complicated problems. Such composite applications commonly are represented as workflows. Workflow scheduling is a well-known optimization problem, for which there is a great amount of solutions. Most of the algorithms contain parameters, which affect the result of a method. Thus, for the efficient scheduling it is important to tune parameters of the algorithms. Moreover, performance models, which are used for the estimation of obtained solutions, are crucial parts of workflow scheduling. In this work we present a combined approach for automatic parameters tuning and performance models construction in the background of the WMS lifecycle. Algorithms tuning is provided by hyper-heuristic genetic algorithm, whereas models construction is performed via symbolic regression methods. Developed algorithm was evaluated using CLAVIRE platform and is applicable for any distributed computing systems to optimize the execution of composite applications. [ABSTRACT FROM AUTHOR]

Abstract: This paper pretends to offer design rules for the parameters adjustment of the Dynamic Matrix Control (DMC) to allow an easier starting up. The effect on the time response of each algorithm parameter that can be tuned by the user is studied in an unconstrained system. To this aim, the position of the closed loop poles of the equivalent system is calculated. To simplify the study and to obtain more direct conclusions the number of poles will be limited using a First Order Plus Death Time simplification of the real plant. Design rules proposed in this study are tested in some simulated benchmarks and in a real plant. [Copyright &y& Elsevier]

Maximum power point tracking technique is adopted in most photovoltaic (PV) power plants in order to improve the power conversion efficiency. However, with the expansion of PV installed capacity, PV systems are facing a series of challenges, including overload, overvoltage, and frequency deviation. In addition, the intermittence of photovoltaic power generation brings uncertain disturbances to the system. Therefore, it is necessary to realize PV constant power generation (CPG) in the presence of grid side and plant side. Traditional CPG algorithm based on perturb & observe is faced with low dynamic performance and large tracking error. To this end, a CPG scheme based on the error-based active disturbance rejection control (EADRC) approach and the perturb & observe method is proposed to obtain faster dynamic performance and lower power oscillation. In the scheme, the operation modes can be switched adaptively, and the operation points can be moved flexibly. In order to achieve optimal performance, the double-quantum chaotic social spider optimization algorithm is put forward to optimize the EADRC parameters. Finally, the feasibility and effectiveness of the proposed EADRC based CPG scheme have been validated experimentally, the results clearly demonstrate the capability of proposed CPG strategy in tracking the power reference with the tracking error of 0.21 % and settling time of 0.019s. • A constant power generation scheme considering disturbance suppression is proposed. • Novel DQCSSO algorithm is put forward to optimize controller parameters. • Flexible operation mode and operation point are realized. • Fast dynamic response and low power oscillation are obtained by proposed scheme. [ABSTRACT FROM AUTHOR]

Abstract: The most commonly used parameters selection method for support vector machines (SVM) is cross-validation, which needs a long-time complicated calculation. In this paper, a novel regularization parameter and a kernel parameter tuning approach of SVM are presented based on quantum-inspired evolutionary algorithm (QEA). QEA with quantum chromosome and quantum mutation has better global search capacity. The parameters of least squares support vector machines (LS-SVM) can be adjusted using quantum-inspired evolutionary optimization. Classification and function estimation are studied using LS-SVM with wavelet kernel and Gaussian kernel. The simulation results show that the proposed approach can effectively tune the parameters of LS-SVM, and the improved LS-SVM with wavelet kernel can provide better precision. [Copyright &y& Elsevier]

This paper presents an application of Ant Colony Optimization (ACO) algorithm to tune the parameters in the design of a novel type of nonlinear proportional-integral-differential (NLPID) controller, which is used in flight simulator. A differential tracker and a reference generator are included in the NLPID controller. The differential tracker is used to track output position and its differential, while the reference generator is used to generate reference input signals. ACO algorithm is a novel heuristic algorithm, which is based on the process of real ants in the nature searching for food. In order to tune the parameters of the NLPID controller using the new proposed grid-based ACO algorithm, an objective function based on position tracing error is constructed. The parameters tuning of NLPID can be summed up as the typical continual spatial optimization problem, grid-based searching strategy is adopted in the improved ACO algorithm, and self-adaptive control strategy for the pheromone decay parameter is also adopted. In order to enhance the searching speed, pair ants which searched from two different mesh points at the same time are used. The body structure and control system architecture of a type of flight simulator with the grid-based ACO algorithm optimized NLPID are also proposed. The simulation results demonstrate that both for the standard and random input signals, the tracking error is very small, and the whole control system with grid-based ACO algorithm optimized NLPID has quick response performance and strong robustness. [ABSTRACT FROM AUTHOR]