A Bio-inspired algorithm to solve Dynamic Multi-Objective Optimization Problems
Year: 2018
Type: dissertation
Abstract: Many different applications in engineering, science and industry have a considerable degree of complexity, and sometimes this complexity may be based on the presence of multiple conflicting objective functions, which must be simultaneously optimized. These kinds of problems are the so-called multi-objective optimization problems (MOPs). However, in everyday life, most optimization problems are not static in nature and usually have at least one objective that can change over time. In recent years, MOPs in dynamic environments have attracted some research efforts. However, most research focuses on either static multi-objective optimization or dynamic single- bjective optimization. Therefore, not much research has been done on Dynamic Multi-Objective Optimization (DMO). Evolutionary algorithms and Artificial Immune System (AIS) have been popular to solve dynamic single objective optimization problems. Nevertheless, such combination has been scarcely explored when solving DMOPs. On the other hand, a few Differential Evolution(DE)-based algorithms have been proposed. In this thesis, two Differential Evolution-based algorithms to solve dynamic multi-objective optimization problems (DMOPs) are proposed.
Source: DSpace (Universidad Veracruzana)
Authors: Martínez Peñaloza, María Guadalupe
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3014104712