accurate and effective electricity price forecasting is critical to market participants in order to make an appropriate risk management in competitive electricity markets. market participants rely on price forecasts to decide on their bidding strategies, allocate assets and plan facility investments. however, due to its time variant behavior and non-linear and non-stationary nature, electricity...

In this paper, a hybrid method for solving multi-objective problem has been provided. The proposed method is combining the ε-Constraint and the Cuckoo algorithm. First the multi objective problem transfers into a single-objective problem using ε-Constraint, then the Cuckoo optimization algorithm will optimize the problem in each task. At last the optimized Pareto frontier will be drawn. The adv...

This paper deals with application of evolutionary algorithm (EA) to solve optimal power flow problem in an efficient manner. In this paper a new approach using cuckoo search (CS) method is proposed for solving OPF problem by optimal setting of control variables. Cuckoo search method is a bio-inspired algorithm based on brooding behaviour of cuckoo birds. This algorithm can search for a global s...

Precedence constrained sequencing problem (PCSP) is related to locate the optimal sequence with the shortest traveling time among all feasible sequences. In PCSP, precedence relations determine sequence of traveling between any two nodes. Various methods and algorithms for effectively solving the PCSP have been suggested. In this paper we propose a cuckoo search algorithm (CSA) for effectively ...

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...

Hard optimization problems that cannot be solved within reasonable time by standard, mathematical, deterministic methods are of great practical interest. Metaheuristics inspired by nature were recently successfully used for such problems. These metaheuristics are based on random Monte-Carlo search guided by simulation of some nature intelligence, especially evolution and swarm intelligence. One...

Cuckoo hashing is a hash table data structure introduced in [1], that offers constant worst case search time. As a major contribution of this paper, we analyse modified versions of this algorithm with improved performance. Further, we provide an asymptotic analysis of the search costs of all this variants of cuckoo hashing and compare this results with the well known properties of double hashin...

In this paper, a comparative study is carried using three nature-inspired algorithms namely Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Cuckoo Search (CS) on clustering problem. Cuckoo search is used with levy flight. The heavy-tail property of levy flight is exploited here. These algorithms are used on three standard benchmark datasets and one real-time multi-spectral satelli...

In this paper a new evolutionary algorithm, for continuous nonlinear optimization problems, is surveyed. This method is inspired by the life of a bird, called Cuckoo. The Cuckoo Optimization Algorithm (COA) is evaluated by using the Rastrigin function. The problem is a non-linear continuous function which is used for evaluating optimization algorithms. The efficiency of the COA has been studied...