this paper addresses the design of an observer-based fault diagnosis scheme, which is applied to some of the sensors and actuators of a wind turbine benchmark model. the methodology is based on a modified sliding mode observer (smo) that allows accurate reconstruction of multiple sensor or actuator faults occurring simultaneously. the faults are reconstructed using the equivalent output error i...

This paper presents a novel hybrid algorithm named Since Cosine Crow Search Algorithm. To propose the SCCSA, two novel algorithms are considered including Crow Search Algorithm (CSA) and Since Cosine Algorithm (SCA). The advantages of the two algorithms are considered and utilize to design an efficient hybrid algorithm which can perform significantly better in various benchmark functions. The c...

In this paper, stochastic leader gravitational search algorithm (SL-GSA) based on randomized k is proposed. Standard GSA (SGSA) utilizes the best agents without any randomization, thus it is more prone to converge at suboptimal results. Initially, the new approach randomly choses k agents from the set of all agents to improve the global search ability. Gradually, the set of agents is reduced by...

We consider a stochastic control problem of beating a stochastic benchmark. The problem is considered in an incomplete market setting with external economic factors. The investor preferences are modelled in terms of HARA-type utility functions and trading takes place in a finite time horizon. The objective of the investor is to minimize his expected loss from the outperformance of the benchmark...

A new evolutionary algorithm, Elitistic Evolution (termed EEv), is proposed in this paper. EEv is an evolutionary method for numerical optimization with adaptive behavior. EEv uses small populations (smaller than 10 individuals). It have an adaptive parameter to adjust the balance between global exploration and local exploitation. Elitism have great influence in EEv’ proccess and that influence...

This paper deals with the characterization of the fitness landscape of multimodal functions and how it can be used to choose the most appropriate evolutionary algorithm for a given problem. An algorithm that obtains a general description of real valued multimodal fitness landscapes in terms of the relative number of optima, their sparseness, the size of their attraction basins and the evolution...

As Internet expands, the number of application servers, especially Web servers, has been increasing exponentially. To improve the performance of Web servers, researchers have paid attention to and studied the Web server’s macro-performance, namely, the response time and throughput, which can be perceived by end users directly. In this paper, we have produced a micro benchmark, ServBench, by stu...

In this paper we describe improvements to the particle swarm optimizer (PSO) made by inclusion of an unscented Kalman filter to guide particle motion. We demonstrate the effectiveness of the unscented Kalman filter PSO by comparing it with the original PSO algorithm and its variants designed to improve performance. The PSOs were tested firstly on a number of common synthetic benchmarking functi...

Differential evolution (DE) has been shown to be a simple and effective evolutionary algorithm for global optimization both in benchmark test functions and many real-world applications. This paper introduces a dynamic differential evolution (D-DE) algorithm to solve constrained optimization problems. In D-DE, a novel mutation operator is firstly designed to prevent premature. Secondly, the scal...

Cuckoo search is a nature-inspired metaheuristic algorithm, based on the brood parasitism of some cuckoo species, along with Lévy flights random walks. In this paper, a modified version is proposed, where the new solutions generated from the exploration and exploitation phases are combined, evaluated and ranked together, rather than separately in the original algorithm, in addition to imposing ...