This paper investigates three disparate design cases where the newly developed s-Pareto frontier based concept selection paradigm is used to compare competing design concepts under a multiobjective optimization framework. The new paradigm, which was recently introduced by the authors, is based on the principle of Pareto optimality – a principle that defines an important class of optimal solutio...

The notion of Pareto-optimality is one of the major approaches to multiobjective programming. While it is desirable to find more Pareto-optimal solutions, it is also desirable to find the ones scattered uniformly over the Pareto frontier in order to provide a variety of compromise solutions to the decision maker. In this paper, we design a genetic algorithm for this purpose. We compose multiple...

The most important issue in multi-objective optimization problems is to determine the Pareto points along the Pareto frontier. If the optimization problem involves multiple conflicting objectives, the results obtained from the Pareto-optimality will have the trade-off solutions that shaping the Pareto frontier. Each of these solutions lies at the boundary of the Pareto frontier, such that the i...

In this paper, a new algorithm is proposed to solve multi-objective optimization problems (MOOPs) through applying the trust-region (TR) method based on local search (LS) techniques; where the MOOP converting to a single objective optimization problem (SOOP) by using reference point method. In the proposed algorithm, some of the points in the search space are generated. For each point the TR al...

One of the most common approaches for multiobjective optimization is to generate the whole or partial efficient frontier and then decide about the preferred solution in a higher-level decision-making process. In this paper, a new method for generating the efficient frontier for multiobjective problems is developed, called the diversity maximization approach (DMA). This approach is capable of so...

A major goal of smart grid technology (e.g., smart meters) is to provide consumers with demand response signals such as electricity tariff and CO2 footprint so that the consumers can consciously control their electricity consumption patterns. These demand response signals provide incentives for the consumers to help reduce peak energy demand by load balancing, as this is particularly relevant i...

The assignment of multiobjective human resources is a very important phase of the decisionmaking process, especially with respect to research and development projects where performance strongly depends on human resources capabilities. Unfortunately, the input data or related parameters are frequently imprecise / fuzzy owing to incomplete or unobtainable information, which can be represented as ...

Many practical optimization problems usually have several conflicting objectives. In those multi-objective optimization, no solution optimizing all objective functions simultaneously exists in general. Instead, Pareto optimal solutions, which are “efficient” in terms of all objective functions, are introduced. In general we have many Pareto optimal solutions. Therefore, we need to decide a fina...

The multi-objective optimization problem is the main purpose of generating an optimal set of targets known as Pareto optimal frontier to be provided the ultimate decision-makers. The final selection of point of Pareto frontier is usually made only based on the goals presented in the mathematical model to implement the considered system by the decision-makers. In this paper, a mathematical model...

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. ...