Topic
Extremal optimization
About: Extremal optimization is a research topic. Over the lifetime, 1168 publications have been published within this topic receiving 104943 citations.
Papers published on a yearly basis
Papers
More filters
••
01 Nov 2016TL;DR: The experimental results demonstrate clearly that CRO-CARP has superior performance when compared with other existing optimization approaches such as Genetic algorithm, Ant Colony Optimization, Tabu Search and Greedy Randomized Adaptive Search Procedure (GRASP) optimization.
Abstract: Capacitated Arc Routing Problem (CARP) is known as an NP-hard combinatorial optimization problem. Chemical Reaction Optimization (CRO) is a recent metaheuristic inspired by the nature of chemical reactions of molecules and the mechanism of energy redistribution. CRO has been successfully exploited for solving a broad family of continuous and discrete optimization problems. In this paper, we propose CRO-CARP a CRO-based algorithm for solving CARP, where the potential solution of CARP is encoded in a molecule as a sequence of required arcs. The experimental results demonstrate clearly that CRO-CARP has superior performance when compared with other existing optimization approaches such as Genetic algorithm, Ant Colony Optimization, Tabu Search and Greedy Randomized Adaptive Search Procedure (GRASP) optimization.
3 citations
01 Jan 2013
TL;DR: It is suggested that within the context of this exhibition, which aims to provide a chronology of key events in the history of the glass-making industry, the use of these techniques and their applications over a period of a century should be considered to be a new phenomenon.
Abstract: ......................................................................................................................... iii ACKNOWLEDGMENTS .................................................................................................... iv LIST OF TABLES ............................................................................................................... vii LIST OF FIGURES ............................................................................................................ viii
3 citations
••
01 Jul 2017
TL;DR: In this paper, a binary-coded extremal optimization (BCEO) based fractional-order frequency control method for an islanded microgrid was proposed, where an optimal fractionalorder PID (FOPID) controller optimized by a simple but efficiency BCEO algorithm was used for the frequency control of an island-based microgrid.
Abstract: Fractional order control theories have attracted increasing attentions recently due to their better control performance than traditional integer-order controllers, but there are few research works concerning their applications of power systems and power converters. This paper presents a novel binary-coded extremal optimization (BCEO) based fractional-order frequency control method for an islanded microgrid. The basic idea behind the proposed method is using an optimal fractional-order PID (FOPID) controller optimized by a simple but efficiency BCEO algorithm for the frequency control of an islanded microgrid. The superiority of the proposed BCEO-FOPID method to other reported binary-coded genetic algorithm based FOPID/PID, particle swarm optimization based fuzzy FOPID and BCEO based PID algorithms is demonstrated by the simulation results on a typical islanded microgrid.
3 citations
••
28 Jun 2018TL;DR: The hybrid approach (eo-PSO) is tested by the data sets from the emerging stock market of china A as well as the classical approaches of GA and original PSO, indicating that the hybrid approach has superior performance.
Abstract: Complex portfolio selection problems with realistic constraints have being studied by the researchers and practitioners in the financial and economic field. This paper discussed a class of complex portfolio selection problem with cardinality constraints and bonding constraints, which is NP-hard problem, and difficultly tackled by the conventional methods. A heuristic approach, called as particle swarm optimization (PSO), is presented and improved by combination with Extremal optimization (EO). The hybrid approach (eo-PSO) is tested by the data sets from the emerging stock market of china A as well as the classical approaches of GA and original PSO, the comparisons show that eo-PSO is the most competitive. In addition, These results are also investigated by the risk range Theorem (Xue Deng, &Jun-feng Zhao,2013), indicating that the hybrid approach has superior performance.
3 citations