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

2017 ◽  
Vol 31 ◽  
pp. 155-168 ◽  
Author(s):  
Bachir Bentouati ◽  
Saliha Chettih ◽  
Ragab Abdel-Aziz El-Sehiemy
Keyword(s):  
Optimization Problem ◽  
Economic Dispatch ◽  
Optimal Solution ◽  
Operating Conditions ◽  
Krill Herd Algorithm ◽  
Krill Herd ◽  
Conflicting Objectives ◽  
Metaheuristic Search ◽  
Elitism Scheme ◽  
Planning And Operation

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.

scholarly journals Simulated Annealing-Based Krill Herd Algorithm for Global Optimization

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Gai-Ge Wang ◽  
Lihong Guo ◽  
Amir Hossein Gandomi ◽  
Amir Hossein Alavi ◽  
Hong Duan
Keyword(s):  
Global Optimization ◽  
Simulated Annealing ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Greedy Strategy ◽  
Krill Herd Algorithm ◽  
Krill Herd ◽  
Low Probability ◽  
Elitism Scheme ◽  
Reliability And Robustness

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.

scholarly journals A Free Search Krill Herd Algorithm for Functions Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-21 ◽  
Author(s):  
Liangliang Li ◽  
Yongquan Zhou ◽  
Jian Xie
Keyword(s):  
Optimization Algorithm ◽  
Search Strategy ◽  
Optimal Solution ◽  
High Dimensional ◽  
Opposition Based Learning ◽  
Krill Herd Algorithm ◽  
Krill Herd ◽  
Local Optimal Solution ◽  
Free Search ◽  
Low Dimensional

To simulate the freedom and uncertain individual behavior of krill herd, this paper introduces the opposition based learning (OBL) strategy and free search operator into krill herd optimization algorithm (KH) and proposes a novel opposition-based free search krill herd optimization algorithm (FSKH). In FSKH, each krill individual can search according to its own perception and scope of activities. The free search strategy highly encourages the individuals to escape from being trapped in local optimal solution. So the diversity and exploration ability of krill population are improved. And FSKH can achieve a better balance between local search and global search. The experiment results of fourteen benchmark functions indicate that the proposed algorithm can be effective and feasible in both low-dimensional and high-dimensional cases. And the convergence speed and precision of FSKH are higher. Compared to PSO, DE, KH, HS, FS, and BA algorithms, the proposed algorithm shows a better optimization performance and robustness.

scholarly journals Lévy-Flight Krill Herd Algorithm

2013 ◽  
Vol 2013 ◽  
pp. 1-14 ◽  
Author(s):  
Gaige Wang ◽  
Lihong Guo ◽  
Amir Hossein Gandomi ◽  
Lihua Cao ◽  
Amir Hossein Alavi ◽  
...  
Keyword(s):  
Optimization Problems ◽  
Computing Time ◽  
Optimization Methods ◽  
Population Based ◽  
Global Optimum ◽  
Lévy Flight ◽  
Levy Flight ◽  
Krill Herd Algorithm ◽  
Krill Herd ◽  
Elitism Scheme

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.

scholarly journals Evolutionary algorithms for generating urban morphology: Variations and multiple objectives

2018 ◽  
Vol 17 (1) ◽  
pp. 5-35 ◽  
Author(s):  
Mohammed Makki ◽  
Milad Showkatbakhsh ◽  
Aiman Tabony ◽  
Michael Weinstock
Keyword(s):  
Morphological Variation ◽  
Optimal Solution ◽  
Search Space ◽  
Urban Morphology ◽  
Stochastic Search ◽  
Brute Force ◽  
Design Problems ◽  
Conflicting Objectives ◽  
Search Processes ◽  
Metaheuristic Search

Morphological variation of urban tissues, which evolve through the optimisation of multiple conflicting objectives, benefit significantly from the application of robust metaheuristic search processes that utilise search and optimisation mechanisms for design problems that have no clear single optimal solution, as well as a solution search space that is too large for a ‘brute-force’ manual approach. As such, and within the context of the experiments presented within this article, the rapidly changing environmental, climatic and demographic global conditions necessitates the utilisation of stochastic search processes for generating design solutions that optimise for multiple conflicting objectives by means of controlled and directed morphological variation within the urban fabric.

scholarly journals Gbest Artificial Bee Colony for Non-convex Optimal Economic Dispatch in Power Generation

2018 ◽  
Vol 11 (1) ◽  
pp. 187
Author(s):  
M. N. Abdullah ◽  
A. F. A. Manan ◽  
J. J. Jamian ◽  
S. A. Jumaat ◽  
N. H. Radzi
Keyword(s):  
Power System ◽  
Optimization Problem ◽  
Artificial Bee Colony ◽  
Heuristic Algorithms ◽  
Economic Dispatch ◽  
Optimal Solution ◽  
System Optimization ◽  
Bee Colony ◽  
Solution Convergence ◽  
And Performance

Non-convex Optimal Economic Dispatch (OED) problem is a complex optimization problem in power system operation that must be optimized economically to meet the power demand and system constraints. The non-convex OED is due to the generator characteristic such as prohibited operation zones, valve point effects (VPE) or multiple fuel options. This paper proposes a Gbest Artificial Bee Colony (GABC) algorithm based on global best particle (gbest) guided of Particle Swarm Optimization (PSO) in Artificial bee colony (ABC) algorithm for solving non-convex OED with VPE. In order to investigate the effectiveness and performance of GABC algorithm, the IEEE 14-bus 5 unit generators and IEEE 30-bus 6 unit generators test systems are considered. The comparison of optimal solution, convergence characteristic and robustness are also highlighted to reveal the advantages of GABC. Moreover, the optimal results obtained by proposed GABC are compared with other reported results of meta-heuristic algorithms. It found that the GABC capable to obtain lowest cost as compared to others. Thus, it has great potential to be implemented in  different types of power system optimization problem.

A new algorithm for finding CRM-model coefficients

2019 ◽  
Vol 5 (3) ◽  
pp. 164-185 ◽  
Author(s):  
Alexander D. Bekman ◽  
Sergey V. Stepanov ◽  
Alexander A. Ruchkin ◽  
Dmitry V. Zelenin
Keyword(s):  
Approximate Solution ◽  
Optimization Problem ◽  
Optimal Solution ◽  
Complex Form ◽  
Approximate Solutions ◽  
The Other ◽  
Programming Algorithm ◽  
Stochastic Algorithms ◽  
Large Set ◽  
Flow Rates

The quantitative evaluation of producer and injector well interference based on well operation data (profiles of flow rates/injectivities and bottomhole/reservoir pressures) with the help of CRM (Capacitance-Resistive Models) is an optimization problem with large set of variables and constraints. The analytical solution cannot be found because of the complex form of the objective function for this problem. Attempts to find the solution with stochastic algorithms take unacceptable time and the result may be far from the optimal solution. Besides, the use of universal (commercial) optimizers hides the details of step by step solution from the user, for example&nbsp;— the ambiguity of the solution as the result of data inaccuracy.<br> The present article concerns two variants of CRM problem. The authors present a new algorithm of solving the problems with the help of “General Quadratic Programming Algorithm”. The main advantage of the new algorithm is the greater performance in comparison with the other known algorithms. Its other advantage is the possibility of an ambiguity analysis. This article studies the conditions which guarantee that the first variant of problem has a unique solution, which can be found with the presented algorithm. Another algorithm for finding the approximate solution for the second variant of the problem is also considered. The method of visualization of approximate solutions set is presented. The results of experiments comparing the new algorithm with some previously known are given.

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