Given that the basic cuckoo search algorithm is vulnerable to local optimum and unsatisfactory calculation precision, chaotic operator (CO), employed as local algorism to optimize elitist individuals in the population, can effectively enhance the properties of cuckoo search (CS) algorithm. Tested by 15 benchmark high-dimensional functions, the experiment result indicates that chaotic cuckoo sea...

Modifications that introduce parallelization of standard cuckoo search algorithm are proposed in this paper. Basic form of the cuckoo search algorithm has already shown great potential for optimization problems, especially when applied to unconstrained continuous functions. In this paper two aspects of parallelization are proposed. The first one addresses the performance issue, while the second...

Economic Load Dispatch problem (ELD) is one of the most important optimization problems in power system operation and planning. This paper introduces a solution to ELD problem using a new metaheuristic natureinspired algorithm called Cuckoo Search Algorithm (CSA). The proposed approach has been applied to various systems. This algorithm is based on the obligate brood parasitic behavior of some ...

Nature inspired meta-heuristic algorithms are iterative search processes which find near optimal solutions by efficiently performing exploration and exploitation of the solution space. Considering the solution space in a specified region, this work compares performances of Bat, Cuckoo search and Firefly algorithms for unconstrained optimization problems. Global optima are found using various te...

Microgrid (MG) with different technologies in distributed generations (DG) and different control facilities require proper management and scheduling strategies. In these strategies, in order to reach the optimal management, the stochastic nature of some decision variables should be considered. Therefore, we proposes a new stochastic-based method for optimal daily energy management (SDEM) of MGs...

In this paper, an improved method based on evolutionary algorithm for speech signal denoising is proposed. In this approach, the stochastic global optimization techniques such as Artificial Bee Colony(ABC), Cuckoo Search (CS)algorithm, and Particle Swarm Optimization (PSO) technique are exploited for learning the parameters of adaptive filtering function required for optimum performance. It was...

Training neural networks is a complex task that is important for supervised learning. A few metaheuristic optimization techniques have been applied to increase the effectiveness of the training process. The Cuckoo Search (CS) algorithm is a recently developed meta-heuristic optimization algorithm which is suitable for solving optimization problems. In this paper, Cuckoo search is implemented in...

In this paper, Hybridization of Cuckoo Search algorithm with Powell Search (HCSPS) is used to solve optimal reactive power problem. Cuckoo search (CS) has been recently projected as a population-based optimization algorithm and it is has so far been efficaciously applied in a variety of fields. The inertia weight of Levy flights is presented to balance the capability of global and local search....

The standard cuckoo search algorithm is of low accuracy and easy to fall into local optimal value in the later evolution. In this paper, an improved cuckoo algorithm is proposed. Dynamic change of parameter of probability is introduced to improve the convergence speed. Complex method is quoted to improve the capabilities of local search algorithm. A non-fixed multi-segment mapping penalty funct...

In this paper, Optimization is considered as the main impact of insight problem and heuristic methods. A proposed method is represented by using two optimization algorithms; cuckoo optimization; is heuristic method and Genetic algorithm; is meta-heuristic method in order to increase the optimization level and speed of calculation as possible. The proposed methodology and technique still a subje...