In this paper multi-objective genetic algorithms were employed for Pareto approach optimization of turboprop engines. The considered objective functions are used to maximize the specific thrust, propulsive efficiency, thermal efficiency, propeller efficiency and minimize the thrust specific fuel consumption. These objectives are usually conflicting with each other. The design variables consist ...

Post-Pareto Optimality Analysis to Efficiently Identify Promising Solutions for Multi-Objective Problems Heidi A. Taboada and David W. Coit Department of Industrial and Systems Engineering, Rutgers University, 96 Frelinghuysen Rd. Piscataway, NJ 08854, USA ABSTRACT: Techniques have been developed and demonstrated to efficiently identify particularly promising solutions from among a Pareto-optim...

A self-adaptive Pareto Evolutionary Multiobjective Optimization (EMO) algorithm based on differential evolution is proposed for evolving locomotion controllers in an artificially embodied legged creature. The objective of this paper is to demonstrate the trade-off between quality of solutions and computational cost. We show empirically that evolving controllers using the proposed algorithm incu...

The aggregation of objectives in multiple criteria programming is one of the simplest and widely used approach. But it is well known that these techniques sometimes fail in different aspects for determining the Pareto frontier. This paper proposes a new line search based approach for multicriteria optimization. The objectives are aggregated and the problem is transformed into a single objective...

This work evaluates two different approaches for multicriteria graph search problems using compromise preferences. This approach focuses search on a single solution that represents a balanced tradeoff between objectives, rather than on the whole set of Pareto optimal solutions. We review the main concepts underlying compromise preferences, and two main approaches proposed for their solution in ...

Evolutionary Artificial Neural Networks (EANN) have been a focus of research in the areas of Evolutionary Algorithms (EA) and Artificial Neural Networks (ANN) for the last decade. In this paper, we present an EANN approach based on pareto multi-objective optimization and differential evolution augmented with local search. We call the approach Memetic Pareto Artificial Neural Networks (MPANN). W...

The Pareto front reflects the optimal trade-offs between conflicting objectives and can be used to quantify the effect of different beam configurations on plan robustness and dose-volume histogram parameters. Therefore, our aim was to develop and implement a method to automatically approach the Pareto front in robust intensity-modulated proton therapy (IMPT) planning. Additionally, clinically r...

Pareto optimization solves a constrained optimization task by reformulating the task as a bi-objective problem. Pareto optimization has been shown quite effective in applications; however, it has little theoretical support. This work theoretically compares Pareto optimization with a penalty approach, which is a common method transforming a constrained optimization into an unconstrained optimiza...

Traditionally, researchers in decision making have focused on attempting to reach Pareto Optimality using horizontal approaches, where optimality is calculated taking into account every participant at the same time. Sometimes, this may prove to be a difficult task (e.g., conflict, mistrust, no information sharing, etc.). In this paper, we explore the possibility of achieving Pareto Optimal outc...

In this paper, we present a pareto multi–objective approach for evolving the behavior and brain (an artificial neural network (ANN)) of embodied artificial creatures. We will attempt to simultaneously minimize the network size while maximizing horizontal locomotion. A variety of network sizes and behaviors were generated by the pareto approach. The best networks exhibited a higher level of sens...