Multi-objective optimization (MO) is a highly demanding research topic because many realworld optimization problems consist of contradictory criteria or objectives. Considering these competing objectives concurrently, a multi-objective optimization problem (MOP) can be formulated as finding the best possible solutions that satisfy these objectives under different tradeoff situations. A family o...

For continuous multi-objective optimization problems there exists an infinite number of solutions on the Paretooptimal front. A multi-objective evolutionary algorithm attempts to find a representative set of the Pareto-optimal solutions. In the case of multi-objective multi-modal problems, there exist multiple decision vectors which map to identical objective vectors on Pareto front. Many multi...

Most of the engineering problems are modeled as evolutionary multiobjective optimization problems, but they always ask for only one best solution, not a set of Pareto optimal solutions. The decision maker’s subjective information plays an important role in choosing the best solution from several Pareto optimal solutions. Generally, the decision-making processing is implemented after Pareto opti...

This paper presents a multi-objective differential evolution algorithm, called MODE, to search for a set of non-dominated solutions on the Pareto front. During the iterative search process, the non-dominated solutions found are stored as the ‘Elite group’ of solutions. The study focuses on utilising the solutions in the Elite group to guide the movement of the search. Several potential mutation...

Portfolio optimization is a problem that lends itself naturally to multiobjective approaches, e.g., aimed to maximize the return of the investment, simultaneously minimizing the risk. The selection of an actual portfolio requires exercising a decision-making process on the set of efficient solutions thus obtained. In this work we consider the case in which knowledge of this selection criterion ...

This study explored the application of a multi-objective evolutionary algorithm (MOEA) and Pareto ordering in the multiple-objective automatic calibration of the Soil and Water Assessment Tool (SWAT). SWAT was calibrated in the Calapooia watershed, Oregon, USA, with two different pairs of objective functions in a cluster of 24 parallel computers. The non-dominated sorting genetic algorithm (NSG...

Multi-objective optimization algorithms aim at finding Pareto-optimal solutions. Recovering Pareto fronts or Pareto sets from a limited number of function evaluations are challenging problems. A popular approach in the case of expensive-to-evaluate functions is to appeal to metamodels. Kriging has been shown efficient as a base for sequential multi-objective optimization, notably through infill...

A tabu search algorithm is proposed for finding the Pareto solutions of multiobjective optimal design problems. In this paper, the contact theorem is used to evaluate the Pareto solutions. The ranking selecting approach and the fitness sharing function are also introduced to identify new current points to begin every iteration cycle. Detailed numerical results are reported in this paper to demo...