In multicriteria optimization, a compromise solution is a feasible solution whose cost vector minimizes the distance to the ideal point w.r.t. a given norm. The coordinates of the ideal point are given by the optimal values for the single optimization problem for each criterion. We show that the concept of compromise solutions fits nicely into the existing notion of Pareto optimality: For a hug...

This paper proposes a multiobjective harmony search (MOHS) algorithm for optimal power flow (OPF) problem. OPF problem is formulated as a nonlinear constrained multiobjective optimization problem where different objectives and different constraints have been considered. Fast elitist non dominated sorting and crowding distance have been used to find and manage the Pareto optimal front. Finally, ...

We deal with the problem of minimizing the expectation of a real valued random function over the weakly Pareto or Pareto set associated with a Stochastic MultiObjective Optimization Problem (SMOP) whose objectives are expectations of random functions. Assuming that the closed form of these expectations is difficult to obtain, we apply the Sample Average Approximation method (SAA-N, where N is t...

Multi-issue negotiation has been studied widely. Our work focuses on the important special case of negotiation with multiple interdependent issues, in which agent utility functions are nonlinear. In this paper, we propose a Distributed Mediator Protocol (DMP) for securely finding Pareto optimal agreements . We also propose a measure for selecting final agreements from the set of Pareto-optimal ...

Formulation of an automatic calibration strategy for the MIKE 11/NAM rainfall–runoff model is outlined. The calibration scheme includes optimisation of multiple objectives that measure different aspects of the hydrograph: (1) overall water balance, (2) overall shape of the hydrograph, (3) peak flows, and (4) low flows. An automatic optimisation procedure based on the shuffled complex evolution ...

The formation of project portfolio is a multi-objective problem that has a high impact on public and private organizations, and has generally been addressed by evolutionary algorithms. They often seek an approximation of the Pareto front, and then the decision maker must choose an only solution from the set. This is not a difficult task when you have to select a solution from a small set evalua...

When solving multiobjective optimization problems, there is typically a decision maker (DM) who is responsible for determining the most preferred Pareto optimal solution based on his preferences. To gain confidence that the decisions to be made are the right ones for the DM, it is important to understand the trade-offs related to different Pareto optimal solutions. We first propose a trade-off ...

Multi-objective genetic algorithm (GAs) is used for pump design pareto optimization, competing objectives for centrifugal pump design are total head (H), input power (Ps), hydraulic efficiency ( H η ), and input parameter are capacity (Q), and the outer radius of the impeller ( ). Multi-objective presents a set of compromised solution, and provides non-dominated optimal choices for designer. 2 ...

In this work, the optimization of control parameters for an active suspension system is considered. Here, two objectives – energy and comfort – play an important role. Thus, a multiobjective optimization problem is formulated which can be solved numerically with a set-oriented approach, for example. The result is the set of optimal compromises of the objectives, the so-called Pareto set. In cas...

In design situations where a single solution must be selected, it is often desirable to present the designer with a smart Pareto set of solutions—a minimal set of nondominated solutions that sufficiently represents the tradeoff characteristics of the design space. These sets are generally created by finding many well-distributed solutions and then either filtering out the excess ones or searchi...