In this study, a hybrid approach combining trust region (TR) algorithm and particle swarm optimization (PSO) is proposed to solve multi-objective optimization problems (MOOPs). The proposed approach integrates the merits of both TR and PSO. Firstly, the MOOP converting by weighted method to a single objective optimization problem (SOOP) and some of the points in the search space are generated. ...

This paper presents an approach to incorporate Pareto dominance into the differential evolution (DE) algorithm in order to solve optimization problems with more than one objective by using the DE algorithm. Unlike the existing proposals to extend the DE to solve multiobjective optimization problems, our algorithm uses an external archive to store nondominated solutions. In order to generate tri...

We consider a two-user multiple-input multiple-output (MIMO) interference channel (IC), where a single data stream is transmitted and each receiver applies the minimum mean square error (MMSE) filter. In this paper, we study an open topic on the Pareto boundary of the rate region. The Pareto boundary is divided by two turning points into the weak Pareto boundary (including the horizontal part a...

A multi-objective optimization (MOO) of two-element wing models with morphing flap by using computational fluid dynamics (CFD) techniques, artificial neural networks (ANN), and non-dominated sorting genetic algorithms (NSGA II), is performed in this paper. At first, the domain is solved numerically in various two-element wing models with morphing flap using CFD techniques and lift (L) and drag ...

In the last two decades, multiobjective optimization has become mainstream because of its wide applicability in a variety of areas such engineering, management, the military and other fields. Multi-Objective Evolutionary Algorithms (MOEAs) play a dominant role in solving problems with multiple conflicting objective functions. They aim at finding a set of representative Pareto optimal solutions ...

Many of the standard optimization algorithms focus on optimizing a single, scalar feedback signal. However, real-life optimization problems often require a simultaneous optimization of more than one objective. In this paper, we propose a multi-objective extension to the standard X -armed bandit problem. As the feedback signal is now vector-valued, the goal of the agent is to sample actions in t...

In some real applications of Statistical Process Control it is necessary to design a control chart to not detect small process shifts, but keeping a good performance to detect moderate and large shifts in the quality. In this work we develop a new quality control chart, the synthetic T control chart, which can be designed to cope with this objective. A multi-objective optimization is carried ou...

As current electric power communication network planning can hardly consider multiple design objectives simultaneously, we proposed a general optimization model of multiple objectives optical network planning in electric power communication systems, based on Pareto optimization and genetic algorithms. The optical network in power system is modelled mathematically and the total cost functional i...

In this paper, an efficient multi-objective artificial bee colony optimization algorithm based on Pareto dominance called PC_MOABC is proposed to tackle the QoS based route optimization problem. The concepts of Pareto strength and crowding distance are introduced into this algorithm, and are combined together effectively to improve the algorithm’s efficiency and generate a set of evenly distrib...

The use of Surrogate Based Optimization (SBO) is widely spread in engineering design to reduce the number of computational expensive simulations. However, “real-world” problems often consist of multiple, conflicting objectives leading to a set of competitive solutions (the Pareto front). The objectives are often aggregated into a single cost function to reduce the computational cost, though a b...