Pareto optimization combines independent objectives by computing the Pareto front of its search space, defined as the set of all candidates for which no other candidate scores better under both objectives. This gives, in a precise sense, better information than an artificial amalgamation of different scores into a single objective, but is more costly to compute. We define a general Pareto produ...

Many real-world optimization problems have several, usually conflicting objectives. Evolutionary multi-objective optimization usually solves this predicament by searching for the whole Pareto-optimal front of solutions, and relies on a decision maker to finally select a single solution. However, in particular if the number of objectives is large, the number of Pareto-optimal solutions may be hu...

Recently, a convergence proof of stochastic search algorithms toward finite size Pareto set approximations of continuous multi-objective optimization problems has been given. The focus was on obtaining a finite approximation that captures the entire solution set in some suitable sense, which was defined by the concept of epsilon-dominance. Though bounds on the quality of the limit approximation...

beneficial management practices (bmps) are important measures for reducing agricultural non-point source (nps) pollution. however, selection of bmps for placement in a watershed requires optimizing available resources to maximize possible water quality benefits. due to its iterative nature, the optimization typically takes a long time to achieve the bmp trade-off results which is not desirable ...

In this paper, a new multiobjective genetic algorithm is employed to support the design of a hydraulic actuation system. First, the proposed method is tested using benchmarks problems gathered from the literature. The method performs well and it is capable of identifying multiple Pareto frontiers in multimodal function spaces. Secondly, the method is applied to a mixed variable design problem w...

Multi-objective genetic algorithms (MOGA) are used to optimize a low-thrust spacecraft control law for orbit transfers around a central body. A Lyapunov feedback control law called the Q-law is used to create a feasible orbit transfer. Then, the parameters in the Q-law are optimized with MOGAs. The optimization goal is to minimize both the flight time and the consumed propellant mass of the tra...

A wind turbine transformer (WTT) is designed using a 3D wound core while the transformer’s total owning cost (TOC) and its inrush current performance realized as the two objective functions in a multi-objective optimization process. Multi-objective genetic algorithm is utilized to derive Pareto optimal solutions. The effects of inrush current improvement on other operating and design parameters...

The optimization of multiple conflictive objectives at the same time is a hard problem. In most cases, a uniform distribution of solutions on the Pareto front is the main objective. We propose a novel evolutionary multi-objective algorithm that is based on the selection with regard to equidistant lines in the objective space. The so-called rakes can be computed efficiently in high dimensional o...

Design of any complex system entails many objectives to reach and constraints to satisfy. This multi–objective nature of the problem ensures that the technology solution is always a compromise between conflicting objectives. The purpose of this paper is to demonstrate the application of Niched Pareto genetic algorithm as a relatively fast and straightforward method for obtaining technology sets...