This paper presents a meta-objective optimization approach, called Bi-Goal Evolution (BiGE), to deal with multi-objective optimization problems with many objectives. In multi-objective optimization, it is generally observed that 1) the conflict between proximity and diversity requirements is aggravated with the increase of the number of objectives and 2) the Pareto dominance loses its effective...

The design and operations of energy systems are key issues for matching energy supply and consumption. Several optimization methods based on the mixed integer linear programming (MILP) have been developed for this purpose. However, due to uncertainty of some parameters like market conditions and resource availability, analyzing only one optimal solution with mono objective function is not su ci...

In this paper, we integrate goal programming (GP), Taylor Series, Kuhn-Tucker conditions and Penalty Function approaches to solve linear fractional bi-level programming (LFBLP)problems. As we know, the Taylor Series is having the property of transforming fractional functions to a polynomial. In the present article by Taylor Series we obtain polynomial objective functions which are equivalent...

In this article, an effective method to control a power system during emergency conditions is presented. Based on Fuzzy Linear Programming (FLP), a new technique is developed to solve the Load Shedding and Generation Reallocation (LSGR) optimization Problem. The objective function consists of terms of load curtailments and deviations in generation schedules. The constraints are power system var...

in a region, optimal allocation of lands to various agricultural products is one of the most important issue associated with optimal use of agricultural resources. meta-goal programming is one of the multiple criteria decision models. this programming provides more flexible decisions for decision makers than other goal programming. in the current study, optimal cropping pattern was determined u...

This paper contains a complete and updated review of the literature for multiobjective flowshop problems which are among the most studied environments in the scheduling research area. No previous comprehensive reviews exist in the literature. Papers about lexicographical, goal programming, objective weighting and Pareto approaches have been reviewed. Exact, heuristic and metaheuristic methods h...

Dynamic real-time optimization (DRTO) systems sometimes fail when solving intrinsic optimization problems. There are situations where the solution is infeasible due to the initial conditions, constraint changes during operation, or even the presence of conflicts on constraint specifications. By using a goal programming approach, this work proposes a method to solve these infeasibilities by refo...

In some practical situations the decision maker is interested in setting multi aspiration levels for objectives that may not be expressed in a specific manner. So in this paper, Atanassov’s intuitionistic fuzzy transportation problem with multi-item, multi-objective function assuming multiple choices is considered. We have modeled multi-objective multi-choice multi-item Atanassov’s intuitionist...

In multiobjective optimisation, one of the most common ways of describing the decision makerÕs preferences is to assign targeted values (goals) to con ̄icting objectives as well as relative weights and priority levels for attaining the goals. In linear and convex decision situations, traditional goal programming provides a pragmatic and ̄exible manner to cater for the above preferences. In certa...

Human-humanoid collaborative tasks require that the robot take into account the goals of the task, interaction forces with the human, and its own balance. We present a formulation for a real-time humanoid controller which allows the robot to keep itself stable, while also assisting the human in achieving their shared objectives. This is achieved with a multirobot quadratic program controller, w...