Properties of objective functions and search algorithms in multi-objective optimization problems

Objectives . A frequently used method for obtaining Pareto-optimal solutions is to minimize a selected quality index under restrictions of the other indices, whose values are thus preset. For scalar objective function, global minimum sought that contains restricted indices as penalty terms. However, landscape such function has steep-ascent areas, which significantly complicate search minimum. This work compared results various heuristic algorithms in solving problems this type. In addition, possibility using sequential quadratic programming (SQP) method, not imposed terms, but included into Lagrange was investigated. Methods The experiments were conducted two analytically defined functions and encountered multi-objective optimization characteristics analog filters. corresponding realized MATLAB environment. Results only algorithm shown obtain optimal all particle swarm algorithm. applicable one filter functions, well appearing be superior speed accuracy search. found incapable finding correct solutions. Conclusions topical problem estimation applicability considered methods based on preliminary analysis properties determine indices.