In the field of multiobjective optimization, important efforts have been made in recent years to generate global Pareto fronts uniformly distributed. A new multiobjective evolutionary algorithm, called ↗−MOGA, has been designed to converge towards ΘP , a reduced but well distributed representation of the Pareto set ΘP . The algorithm achieves good convergence and distribution of the Pareto fron...

Genetic programming (GP) is extensively used for structure and parameter tuning of control systems. This paper describes an application of genetic programming for Pareto biobjective design of polynomial based controllers for some common time-delay systems. In this way, a multi-objective uniform-diversity genetic programming proposed and used for polynomial controller evolution. The ε-eliminatio...

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...

Pareto optimality is capable of striking the optimal trade-off amongst the diverse conflicting QoS requirements of routing in wireless multihop networks. However, this comes at the cost of increased complexity owing to searching through the extended multi-objective search-space. We will demonstrate that the powerful quantum-assisted dynamic programming optimization framework is capable of circu...

This paper proposes a novel approach to generate a uniform distribution of the optimal solutions along the Pareto frontier. We make use of a standard mathematical technique for optimization namely line search and adapt it so that it will be able to generate a set of solutions uniform distributed along the Pareto front. To validate the method, numerical bi-criteria examples are considered. The m...

Multicriteria optimization applications can be implemented using Pareto optimization techniques including evolutionary Multicriteria optimization algorithms. Many real world applications involve multiple objective functions and the Pareto front may contain a very large number of points. Choosing a solution from such a large set is potentially intractable for a decision maker. Previous approache...

In this paper we present a new image thresholding method based on a multiobjective Genetic Algorithm using the Pareto optimality approach. We aim to optimize multiple criteria in order to increase the segmentation quality. Thus, we’ve adapted the well known Non Domination Sorting Genetic Algorithm for this purpose so that it takes into consideration the contribution of the objective functions i...

Transactions with quantitative values are commonly seen in real-world applications. Fuzzy mining algorithms have thus been developed recently to induce linguistic knowledge from quantitative databases. In fuzzy data mining, the membership functions have a critical influence on the final mining results. How to effectively decide the membership functions in fuzzy data mining thus becomes very imp...

The most recent approaches of multi-objective optimization constitute application of meta-heuristic algorithms for which, parameter tuning is still a challenge. The present work hybridizes swarm intelligence with fuzzy operators to extend crisp values of the main control parameters into especial fuzzy sets that are constructed based on a number of prescribed facts. Such parameter-less particle ...

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 ...