Several indicator-based evolutionary multiobjective optimization algorithms have been proposed in the literature. The notion of optimal μ-distributions formalizes the optimization goal of such algorithms: find a set of μ solutions that maximizes the underlying indicator among all sets with μ solutions. In particular for the often used hypervolume indicator, optimal μ-distributions have been the...

The paper presents main features of the conic scalarization method in multiobjective optimization. The conic scalarization method guarantee to generate all proper efficient solutions and does not require any kind of cenvexity or boundedness conditions. In addition the preference and reference point information of the decision maker is taken into consideretion by this method. Also in this paper,...

In this paper we consider a multiobjective optimization problem, and we prove Mond-Weir duality results under second-and higher-order conditions of the objective and constraint functions.

In this paper, we present a study on the sensitivity of aggregation methods with respect to the weights associated with objective functions of a multiobjective optimization problem. To do this study, we have developped some measurements such as the speed metric or the distribution metric. We have performed this study on a set of biobjective optimization test problems: a convex, a non-convex, a ...

in this paper, multi-objective uniform-diversity genetic algorithm (muga) with a diversity preserving mechanism called the ε-elimination algorithm is used for pareto optimization of 5-degree of freedom vehicle vibration model considering the five conflicting functions simultaneously. the important conflicting objective functions that have been considered in this work are, namely, vertical accel...

Article history: Received 7 May 2017 Received in revised form 1 July 2017 Accepted 27 July 2017 Available online 2 August 2017

In problem of portfolio selection, financial Decision Makers (DMs) explain objectives and investment purposes in the frame of multi-objective mathematic problems which are more consistent with decision making realities. At present, various methods have introduced to optimize such problems. One of the optimization methods is the Compromise Programming (CP) method. Considering increasing importan...

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