Metaheuristic algorithm is one of the most popular methods in solving many optimization problems. This paper presents a new hybrid approach comprising of two natures inspired metaheuristic algorithms i.e. Cuckoo Search (CS) and Accelerated Particle Swarm Optimization (APSO) for training Artificial Neural Networks (ANN). In order to increase the probability of the egg’s survival, the cuckoo bird...

Because of computational drawbacks of conventional numerical methods in solving complex optimization problems, researchers may have to rely on meta-heuristic algorithms. Particle swarm optimization (PSO) is one of the most widely used algorithms due to its simplicity of implementation and fast convergence speed. Also, the cuckoo search algorithm is a recently developed meta-heuristic optimizati...

Cuckoo Search (CS) algorithm is a metaheuristic optimization algorithm developed by Xing-she Yang and Suash Deb in 2009. This paper explores the various research work done in application of Cuckoo Search algorithm in optimization of geometry, processes, signals and systems in real world engineering problems. Various researchers have applied Cuckoo Search algorithm in the following broad domains...

in the science of operation research and decision theory, selection is the most important process. selection is a process that studies multiple qualitative and quantitative criteria, related to the science of management, which are mostly incompatible with each other. the multi criteria selection of a renewable energy portfolio is one of the main issues considered in multi criteria literature. i...

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 minimum crossing number problem is among the oldest and most fundamental problems arising in the area of automatic graph drawing. In this paper, eight population-based meta-heuristic algorithms are utilized to tackle the minimum crossing number problem for two special types of graphs, namely complete graphs and complete bipartite graphs. A 2-page book drawing representation is employed for ...

In order to improve convergence velocity and optimization accuracy of the cuckoo search (CS) algorithm for solving the function optimization problems, a new improved cuckoo search algorithm based on the repeat-cycle asymptotic self-learning and self-evolving disturbance (RC-SSCS) is proposed. A disturbance operation is added into the algorithm by constructing a disturbance factor to make a more...

This paper presents an object-oriented software system that implements a cuckoo search (CS) metaheuristic for unconstrained optimization problems. Yang and Deb developed cuckoo search algorithm in MATLAB and tested it on some standard benchmark functions as well as on some engineering optimization problems where it showed promising results. We developed our algorithm in JAVA programming languag...

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...

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...