Pareto optimization combines independent objectives by computing the Pareto front of its search space, defined as the set of all candidates for which no other candidate scores better under both objectives. This gives, in a precise sense, better information than an artificial amalgamation of different scores into a single objective, but is more costly to compute. We define a general Pareto produ...

Single objective optimization for maximizing total net benefit from farming is presented in this study. Differential evolution algorithm which is a family of evolutionary algorithm for fast optimization is employed for the model. The single objective optimization is used to find a better solution using the results of multi-objective optimization of crop planning where three objectives are consi...

....... The fast and elitist non-dominated sorting genetic algorithm (NSGA-II) contains a mechanism to sort individuals in a multi-objective optimization problem into non-dominated fronts, based on their performance in each optimization variable. When dealing with a bi-objective problem it is possible to carry-out the non-dominated sorting more efficiently, using the new sorting method presente...

There have been signi®cant advances in the theory of goal programming (GP) in recent years, particularly in the area of intelligent modelling and solution analysis. The intention of this paper is to provide an overview of these developments, to detail and assess the current state-of-the-art in the subject, and to highlight areas which seem promising for future research. Modelling techniques suc...

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The paradigm of self-optimization introduces flexible systems of objectives to the area of mechatronics. From this flexiblity new coordination problems arise. Such a problem is the coordination of function modules in such a way that the overall system of objectives is met as good as possible. Multi-agent planning offers mechanisms to provide this coordination. In this paper we introduce the app...

In interactive programming, a choice behaviour of the decision maker may differ depending on a proximity of current solution to satisfactory values of the objectives. An interactive approach proposed in this paper allows the decision maker to use different search principles depending on his/her perception of the achieved values of the objectives and trade-offs. While an analysis of values of th...

Portfolio selection is a usual multiobjective problem. This paper will try to deal with the optimum portfolio for a private investor, taking into account three criteria: return, risk and liquidity. These objectives, in general, are not crisp from the point of view of the investor, so we will deal with them in fuzzy terms. The problem formulation is a goal programming (G.P.) one, where the goals...

Genetic algorithms generally use a fixed problem representation that maps variables of the search space to variables of the problem, and operators of variation that are fixed over time. As a result, the potential to explore combinations of large partial solutions is limited. This is problematic when the search space of a problem is large, and scalability is required. To address this issue, seve...

A Real-Time Optimization (RTO) strategy incorporating the fuzzy sets theory is developed, where the problem constraints obtained from process considerations are treated in fuzzy environment. Furthermore, the objective function is penalized by a fuzzified form of the key process constraints. To enable using conventional optimization techniques, the resulting fuzzy optimization problem is the...