On the benefit of ∈-efficient solutions in multi objective space mission design

In this work we consider multi-objective space mission design problems. We will show that it makes sense from the practical point of view to consider in addition to the (Pareto) optimal solutions also nearly optimal ones since this increases significantly the number of options for the decision maker, whereas the possible loss of such approximate solutions compared to optimal - and possibly even 'better' - ones is dispensable. For this, we will examine several typical problems in space trajectory design - a bi-impulsive transfer from the Earth to the asteroid Apophis and several low-thrust multi-gravity assist transfers - and demonstrate the possible benefit of the novel approach. Further, we will present an evolutionary multi-objective algorithm which is designed for this purpose

Current Status of the EMOO Repository, Including Current and Future Research Trends

In this talk, Ill present some statistics of the EMOO repository (delta.cs.cinvestav.mx/~ccoello/EMOO/), emphasizing some of the trends that have been detected in terms of basic research and applications of multi-objective evolutionary algorithms. For example, Ill discuss the remarkable increase in PhD theses related to EMOO, as well as the number of journal papers and exposure of the area in evolutionary computation conferences. Finally, some (potential) future research trends will also be discussed

Agriculture and trade liberalization in Vietnam

This paper provides an ex-post analysis of the impact of trade liberalization in Vietnam between 1993 and 1998, taking into account regional differences. First, a price pass-through analysis is performed to measure how trade liberalization influence provincial prices. These results are plugged into a farm household model in order to capture the effects on households' outcomes such as quantities produced, agricultural income and profits. An original continuous treatment assessment measures the effects of trade liberalization proportionally to the degree of initial household specialization in export crops. My findings suggest that trade liberalization has differently affected domestic prices and agricultural variables across profits groups and regions. Trade liberalization in agriculture, between 1993 and 1998 has increased inequalities in Vietnam, with a negative evolution of agricultural profits for the poorest.trade liberalization ; agriculture ; price pass-through

Constraint handling strategies in Genetic Algorithms application to optimal batch plant design

Optimal batch plant design is a recurrent issue in Process Engineering, which can be formulated as a Mixed Integer Non-Linear Programming(MINLP) optimisation problem involving specific constraints, which can be, typically, the respect of a time horizon for the synthesis of various products. Genetic Algorithms constitute a common option for the solution of these problems, but their basic operating mode is not always wellsuited to any kind of constraint treatment: if those cannot be integrated in variable encoding or accounted for through adapted genetic operators, their handling turns to be a thorny issue. The point of this study is thus to test a few constraint handling techniques on a mid-size example in order to determine which one is the best fitted, in the framework of one particular problem formulation. The investigated methods are the elimination of infeasible individuals, the use of a penalty term added in the minimized criterion, the relaxation of the discrete variables upper bounds, dominancebased tournaments and, finally, a multiobjective strategy. The numerical computations, analysed in terms of result quality and of computational time, show the superiority of elimination technique for the former criterion only when the latter one does not become a bottleneck. Besides, when the problem complexity makes the random location of feasible space too difficult, a single tournament technique proves to be the most efficient one

Computing the set of Epsilon-efficient solutions in multiobjective space mission design

In this work, we consider multiobjective space mission design problems. We will start from the need, from a practical point of view, to consider in addition to the (Pareto) optimal solutions also nearly optimal ones. In fact, extending the set of solutions for a given mission to those nearly optimal significantly increases the number of options for the decision maker and gives a measure of the size of the launch windows corresponding to each optimal solution, i.e., a measure of its robustness. Whereas the possible loss of such approximate solutions compared to optimal—and possibly even ‘better’—ones is dispensable. For this, we will examine several typical problems in space trajectory design—a biimpulsive transfer from the Earth to the asteroid Apophis and two low-thrust multigravity assist transfers—and demonstrate the possible benefit of the novel approach. Further, we will present a multiobjective evolutionary algorithm which is designed for this purpose

Quantum Gates Between Two Spins in a Triple Dot System with an Empty Dot

We propose a scheme for implementing quantum gates and entanglement between spin qubits in the outer dots of a triple-dot system with an empty central dot. The voltage applied to the central dot can be tuned to realize the gate. Our scheme exemplifies the possibility of quantum gates outside the regime where each dot has an electron, so that spin-spin exchange interaction is not the only relevant mechanism. Analytic treatment is possible by mapping the problem to a t-J model. The fidelity of the entangling quantum gate between the spins is analyzed in the presence of decoherence stemming from a bath of nuclear spins, as well as from charge fluctuations. Our scheme provides an avenue for extending the scope of two qubit gate experiments to triple-dots, while requiring minimal control, namely that of the potential of a single dot, and may enhance the qubit separation to ease differential addressability.Comment: 7 pages, 6 figure

Computing the Set of Approximate Solutions of an MOP with Stochastic Search Algorithms

research reportIn this work we develop a framework for the approximation of the entire set of $\epsilon$-efficient solutions of a multi-objective optimization problem with stochastic search algorithms. For this, we propose the set of interest, investigate its topology and state a convergence result for a generic stochastic search algorithm toward this set of interest. Finally, we present some numerical results indicating the practicability of the novel approach

A Study of the Combination of Variation Operators in the NSGA-II Algorithm

Multi-objective evolutionary algorithms rely on the use of variation operators as their basic mechanism to carry out the evolutionary process. These operators are usually fixed and applied in the same way during algorithm execution, e.g., the mutation probability in genetic algorithms. This paper analyses whether a more dynamic approach combining different operators with variable application rate along the search process allows to improve the static classical behavior. This way, we explore the combined use of three different operators (simulated binary crossover, differential evolution’s operator, and polynomial mutation) in the NSGA-II algorithm. We have considered two strategies for selecting the operators: random and adaptive. The resulting variants have been tested on a set of 19 complex problems, and our results indicate that both schemes significantly improve the performance of the original NSGA-II algorithm, achieving the random and adaptive variants the best overall results in the bi- and three-objective considered problems, respectively.UNIVERSIDAD DE MÁLAGA. CAMPUS DE EXCELENCIA INTERNACIONAL ANDALUCÍA TEC