Floating point genetic algorithms for nonconvex nonlinear programming problems: revised GENOCOP III
Masatoshi Sakawa, Katsuhiro Yauchi · Electronics and Communications in Japan (Part III Fundamental Electronic Science) · 2000
In this paper, we focus on the nonconvex nonlinear programming problem and try to perform optimization using a genetic algorithm that can perform the multipoint parallel search. For such type of nonlinear optimization of constrained problems, Michalewicz and colleagues have recently proposed GENOCOP III, where individual representation using floating point is adopted along with two groups of constraints; one is the reference group where individuals satisfy all of the constraints and the other is the search group where individuals satisfy only linear constraints. However, in GENOCOP III, where initial populations are randomly generated, it is very difficult to determine at least one reference point that satisfies all of the constraints. Moreover, since a new search point is randomly generated on the line joining the search point and the reference point, some problems are encountered that are related to efficient search and processing speed. In this paper, to resolve the problems of GENOCOP III, we propose a method of efficient location of initial reference point by solving the optimization problem where sum of squares of violated nonlinear constraints is used as the objective function. Moreover, the method of search of feasible solution by bisection is proposed. Finally, the effectiveness and validity of the proposed method are shown. © 2000 Scripta Technica, Electron Comm Jpn Pt 3, 83(8): 1–9, 2000