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1. Polygonal Approximation Using an Artificial Bee Colony Algorithm.

Author

Huang, Shu-Chien

Subjects
*POLYGONS, *APPROXIMATION theory, *BEES algorithm, *INTEGRALS, *VECTOR analysis, *COMPUTER science
Abstract

A polygonal approximation method based on the new artificial bee colony (NABC) algorithm is proposed in this paper. In the present method, a solution is represented by a vector, and the objective function is defined as the integral square error between the given curve and its corresponding polygon. The search process, including the employed bee stage, the onlooker bee stage, and the scout bee stage, has been constructed for this specific problem. Most experiments show that the present method when compared with the DE-based method can obtain superior approximation results with less error norm with respect to the original curves. [ABSTRACT FROM AUTHOR]

Published
2015
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2. Development of a Method to Measure the Quality of Working Life Using the Improved Metaheuristic Grasshopper Optimization Algorithm

Author

Alireza Jafari Doudaran, Mostafa Jafari, Ahmad Makui, and Rouzbeh Ghousi

Subjects
Measure (data warehouse), Mathematical optimization, Artificial neural network, Optimization algorithm, Article Subject, Computer science, General Mathematics, General Engineering, Engineering (General). Civil engineering (General), Quality of working life, Reduction (complexity), Development (topology), QA1-939, TA1-2040, Metaheuristic, Mathematics, Bees algorithm
Abstract

This paper provides a method to numerically measure the quality of working life based on the reduction of human resource risks. It is conducted through the improved metaheuristic grasshopper optimization algorithm in two phases. First, a go-to study is carried out to identify the relationship between quality of working life and human resource risks in the capital market and to obtain the factors from quality of working life which reduce the risks. Then, a method is presented for the numerical measurement of these factors using a fuzzy inference system based on an adaptive neural network and a new hybrid method called the improved grasshopper optimization algorithm. This algorithm consists of the grasshopper optimization algorithm and the bees algorithm. It is found that the newly proposed method performs better and provides more accurate results than the conventional one.

Published
2021