An Efficient Chaotic Cuckoo Search Framework for Solving Non-Convex Optimal Power Flow Problem

The optimal power flow (OPF) problem is a very complicated task in power systems. OPF problem has a set of equality and inequality constraints. This paper looks at a chaotic cuckoo search (CCS) algorithm for solving non-convex OPF problem. The proposed CCS is a bio-inspired optimization calculation that is inspired by the behaviour of cuckoos people in nature. The chaotic guide is a variation of qualities combined with CS. A sinusoidal chaotic is integrated with CS algorithm and tested on standard IEEE 30-bus test system to the point of improving its global speed of convergence and enhancing its performance. The elitism scheme is also serves to save the best cuckoo during amid the procedure when updating the cuckoo. The results show clearly the superiority of CCS in searching for the best function values results when compared with well-known metaheuristic search algorithms.

A Chaotic Krill Herd Algorithm for Optimal Solution of the Economic Dispatch Problem

The aim of economic dispatch (ED) problem is to provide an efficient utilization of energy resources to produce economic and secure operating conditions for the planning and operation of a power system. ED is formed as a nonlinear optimization problem with conflicting objectives and subjected to both inequality and equality constraints. An efficient improvement of krill herd (KH) algorithm, a powerful metaheuristic method, has been introduced in this paper. The KH algorithm inspired by the Lagrangian and evolutionary behaviour of the krill people in nature, has been investigated to solve ED problem on 6, 13, 20 and 40 generating units. The proposed chaotic krill herd (CKH)) improvement is done by incorporating the chaos approach to KH algorithm for raising the global convergence speed and for enhancing its performance. The elitism scheme serves to save the best krill during the procedure when updating the krill. The results show clearly the superiority of CKH in searching for the best cost value results when compared with well-known metaheuristic search algorithms.

Simulated Annealing-Based Krill Herd Algorithm for Global Optimization

Recently, Gandomi and Alavi proposed a novel swarm intelligent method, called krill herd (KH), for global optimization. To enhance the performance of the KH method, in this paper, a new improved meta-heuristic simulated annealing-based krill herd (SKH) method is proposed for optimization tasks. A new krill selecting (KS) operator is used to refine krill behavior when updating krill’s position so as to enhance its reliability and robustness dealing with optimization problems. The introduced KS operator involves greedy strategy and accepting few not-so-good solutions with a low probability originally used in simulated annealing (SA). In addition, a kind of elitism scheme is used to save the best individuals in the population in the process of the krill updating. The merits of these improvements are verified by fourteen standard benchmarking functions and experimental results show that, in most cases, the performance of this improved meta-heuristic SKH method is superior to, or at least highly competitive with, the standard KH and other optimization methods.

Lévy-Flight Krill Herd Algorithm

To improve the performance of the krill herd (KH) algorithm, in this paper, a Lévy-flight krill herd (LKH) algorithm is proposed for solving optimization tasks within limited computing time. The improvement includes the addition of a new local Lévy-flight (LLF) operator during the process when updating krill in order to improve its efficiency and reliability coping with global numerical optimization problems. The LLF operator encourages the exploitation and makes the krill individuals search the space carefully at the end of the search. The elitism scheme is also applied to keep the best krill during the process when updating the krill. Fourteen standard benchmark functions are used to verify the effects of these improvements and it is illustrated that, in most cases, the performance of this novel metaheuristic LKH method is superior to, or at least highly competitive with, the standard KH and other population-based optimization methods. Especially, this new method can accelerate the global convergence speed to the true global optimum while preserving the main feature of the basic KH.