Performance of Elephant Herding Optimization Algorithm on CEC 2013 real parameter single objective optimization
نویسندگان
چکیده
Numerous real life problems represents hard optimization problems that cannot be solved by deterministic algorithm. In the past decades various different methods were proposed for these kind of problems and one of the methods are nature inspired algorithms especially swarm intelligence algorithms. Elephant herding optimization algorithm (EHO) is one of the recent swarm intelligence algorithm that has not been thoroughly researched. In this paper we tested EHO algorithm on 28 standard benchmark functions and compared results with particle swarm optimization algorithm. Comparison show that EHO has good characteristics and it outperformed other approach from literature. Key–Words: hard optimization problems, optimization algorithms, swarm intelligence, elephant herding optimization, EHO
منابع مشابه
An Optimization K-Modes Clustering Algorithm with Elephant Herding Optimization Algorithm for Crime Clustering
The detection and prevention of crime, in the past few decades, required several years of research and analysis. However, today, thanks to smart systems based on data mining techniques, it is possible to detect and prevent crime in a considerably less time. Classification and clustering-based smart techniques can classify and cluster the crime-related samples. The most important factor in the c...
متن کاملRanking Results of CEC’13 Special Session & Competition on Real-Parameter Single Objective Optimization
1 Ranking procedure The algorithms presented during the CEC'2013 Special Session & Competition on Real-Parameter Single Objective Optimization were ranked using the procedure described below. The mean ranking values for all algorithms on all problems (28) and dimensions (10D, 30-D, 50-D) are presented in the following figures and tables. 1. For N algorithms (here, N = 21 or = 3 or = 2) the resu...
متن کاملCollective Decision-Making by Bee Colonies as Model for Optimization - the OptBees Algorithm
This paper presents OptBees, a new bee-inspired algorithm for solving continuous optimization problems. Two key mechanisms for OptBees are introduced: 1) a local search step; and 2) a process of dynamic variation of the number of active bees that helps the algorithm to regulate the computational effort spent in the search and to achieve improved results. The performance of the algorithm was eva...
متن کاملEnsemble multi-objective biogeography-based optimization with application to automated warehouse scheduling
This paper proposes an ensemble multi-objective biogeography-based optimization (EMBBO) algorithm, which is inspired by ensemble learning, to solve the automated warehouse scheduling problem. First, a real-world automated warehouse scheduling problem is formulated as a constrained multi-objective optimization problem. Then EMBBO is formulated as a combination of several multi-objective biogeogr...
متن کاملAn Ant Colony approach to forward-reverse logistics network design under demand certainty
Forward-reverse logistics network has remained a subject of intensive research over the past few years. It is of significant importance to be issued in a supply chain because it affects responsiveness of supply chains. In real world, problems are needed to be formulated. These problems usually involve objectives such as cost, quality, and customers' responsiveness and so on. To this reason, we ...
متن کامل