In this paper we present a new genetic algorithm (GA) using pareto partitioning method and apply this GA to solving the design problem of the robust PID controller with two degrees of freedom based on the partial model matching approach. This design problem is formulated as two objective minimax optimization problem. Therefore we need to generate a pareto optimal set that is properly distribute...

This paper describes and demonstrates a new and highly innovative technique that identifies an approximation of the entire bounding surface of the feasible objective region directly, including deep concavities, disconnected regions and the edges of interior holes in the feasible areas. The Pareto front is a subset of the surface of the objective boundary and can be extracted easily. Importantly...

| This paper describes a genetic algorithm using a Pareto partitioning method for multiobjective owshop scheduling. The purpose of the proposed method is to generate a set of non-dominated solutions that is properly distributed in the neighborhood of the trade-o surface. Simulation results show that the GA using the Pareto partitioning method has good performances better than the traditional GA...

This paper presents an adaptive weighted sum method for multiobjective optimization problems. The authors developed the bi-objective adaptive weighted sum method, which determines uniformly-spaced Pareto optimal solutions, finds solutions on non-convex regions, and neglects non-Pareto optimal solutions. However, the method could solve only problems with two objective functions. In this work, th...

Optimization is an important concept in science and engineering. Traditionally, methods are developed for unconstrained and constrained single objective optimization tasks. However, with the increasing complexity of optimization problems in the modern technological real world problems, multi-objective optimization algorithms are needed and being developed. With the advent of evolutionary algori...

So far the focus of almost all multior many-objective performance metrics has been the convergence and distribution of solutions in the objective space (Pareto-surface). Pareto-surface metrics such as IGD, HV, and Spread are simple and provide knowledge about the overall performance of the solution set. However, these measures do not provide any insight into the distribution or spread of a solu...

In multi-objective evolutionary algorithms (MOEAs), the traditional fitness assignment strategy based on Pareto dominance is ineffective in sorting out the highquality solutions when the number of the objective is large. Recently, many scholars have used preference order (PO) ranking approach as an optimality criterion in the ranking stage of MOEAs. The experiment shows that the algorithms equi...