Abstract:
The return rates of loan in bank often have fuzzy random characteristic in many cases,this paper described the return rates as fuzzy random variables,used semivariance as the risk measure method,constructed the optimization model of loan portfolio with fuzzy random return rates under semivariance constraints.The purpose of the model is to maximize the primitive chance measure when the total return rate is no less than the preset value at a given confidence level under semivariance constraints.The hybrid int...

Abstract:
Multiple objective stochastic linear programming is a relevant topic. As a matter of fact, many practical problems ranging from portfolio selection to water resource management may be cast into this framework. Severe limitations on objectivity are encountered in this field because of the simultaneous presence of randomness and conflicting goals. In such a turbulent environment, the mainstay of rational choice cannot hold and it is virtually impossible to provide a truly scientific foundation for an optimal decision. In this paper, we resort to the bounded rationality principle to introduce satisfying solution for multiobjective stochastic linear programming problems. These solutions that are based on the chance-constrained paradigm are characterized under the assumption of normality of involved random variables. Ways for singling out such solutions are also discussed and a numerical example provided for the sake of illustration.

Abstract:
We study resource-constrained project scheduling problems with perturbation on activity durations. With the consideration of robustness and stability of a schedule, we model the problem as a multiobjective optimization problem. Three objectives—makespan minimization, robustness maximization, and stability maximization—are simultaneously considered. We propose a hybrid multiobjective evolutionary algorithm (H-MOEA) to solve this problem. In the process of the H-MOEA, the heuristic information is extracted periodically from the obtained nondominated solutions, and a local search procedure based on the accumulated information is incorporated. The results obtained from the computational study show that the proposed approach is feasible and effective for the resource-constrained project scheduling problems with stochastic durations.

Abstract:
Using Skewness and Kurtosis to adjust critical number of firm's credit grade migration,using Semivariance Absolute Deviation method to measure loan portfolio risks,a portfolio optimization model of banking asset based on the adjusted credit grade and the semivariance absolute deviation is set up.The characteristics of this paper lie on two aspects: Firstly,this paper adjusts critical number of firm's credit grade migration of the normal distribution supposition with Skewness and Kurtosis,so it changes the unreasonable phenomena which taken abnormal distribution yield as normal distribution while deciding critical number of firm's credit grade.The measure precision of loan portfolio risk is improved.Secondly,this paper introduces the Semivariance Absolute Deviation method to measure the loan portfolio risk.It changes the unreasonable idea of taking "excess profit" as the risk in Deviation or Absolut Deviation method of current research.It measures the loan portfolio risk more accurate.

Abstract:
this work presents three of the main evolutionary algorithms: genetic algorithm, evolution strategy and evolutionary programming, applied to microstrip antennas design. efficiency tests were performed, considering the analysis of key physical and geometrical parameters, evolution type, numerical random generators effects, evolution operators and selection criteria. these algorithms were validated through design of microstrip antennas based on the resonant cavity method, and allow multiobjective optimizations, considering bandwidth, standing wave ratio and relative material permittivity. the optimal results obtained with these optimization processes, were confirmed by cst microwave studio commercial package.

Abstract:
This paper is concerned with an evolutionary search for limit cycle operation in a class of nonlinear systems. In the first part, single input single output (SISO) systems are investigated and sinusoidal input describing function (SIDF) is extended to those cases where the key assumption in its derivation is violated. Describing function matrix (DMF) is employed to take into account the effects of higher harmonic signals and enhance the accuracy of predicting limit cycle operation. In the second part, SIDF is extended to the class of nonlinear multiinput multioutput (MIMO) systems containing separable nonlinear elements of any general form. In both cases linearized harmonic balance equations are derived and the search for a limit cycle is formulated as a multiobjective problem. Multiobjective genetic algorithm (MOGA) is utilized to search the space of parameters of theoretically possible limit cycle operations. Case studies are presented to demonstrate the effectiveness of the proposed approach.

Abstract:
Component-based Software Engineering (CBSE) uses components to construct systems, being a means to increase productivity by promoting software reuse and increasing software quality. The process of assembling component is called component composition. Components are themselves compositions of components. This give rise to the idea of composition levels, where a component on level i may be decomposed (using more components) at level i+1 or compositions at level i+1 serves as component at level i. We are approaching the multilevel component composition problem. We formulate the problem as multiobjective, involving 4 objectives. The approach used is an evolutionary computation technique.

Abstract:
Purpose: Current inverse planning methods for IMRT are limited because they are not designed to explore the trade-offs between the competing objectives between the tumor and normal tissues. Our goal was to develop an efficient multiobjective optimization algorithm that was flexible enough to handle any form of objective function and that resulted in a set of Pareto optimal plans. Methods: We developed a hierarchical evolutionary multiobjective algorithm designed to quickly generate a diverse Pareto optimal set of IMRT plans that meet all clinical constraints and reflect the trade-offs in the plans. The top level of the hierarchical algorithm is a multiobjective evolutionary algorithm (MOEA). The genes of the individuals generated in the MOEA are the parameters that define the penalty function minimized during an accelerated deterministic IMRT optimization that represents the bottom level of the hierarchy. The MOEA incorporates clinical criteria to restrict the search space through protocol objectives and then uses Pareto optimality among the fitness objectives to select individuals. Results: Acceleration techniques implemented on both levels of the hierarchical algorithm resulted in short, practical runtimes for optimizations. The MOEA improvements were evaluated for example prostate cases with one target and two OARs. The modified MOEA dominated 11.3% of plans using a standard genetic algorithm package. By implementing domination advantage and protocol objectives, small diverse populations of clinically acceptable plans that were only dominated 0.2% by the Pareto front could be generated in a fraction of an hour. Conclusions: Our MOEA produces a diverse Pareto optimal set of plans that meet all dosimetric protocol criteria in a feasible amount of time. It optimizes not only beamlet intensities but also objective function parameters on a patient-specific basis.

Abstract:
In order to well maintain the diversity of obtained solutions, a new multiobjective evolutionary algorithm based on decomposition of the objective space for multiobjective optimization problems (MOPs) is designed. In order to achieve the goal, the objective space of a MOP is decomposed into a set of subobjective spaces by a set of direction vectors. In the evolutionary process, each subobjective space has a solution, even if it is not a Pareto optimal solution. In such a way, the diversity of obtained solutions can be maintained, which is critical for solving some MOPs. In addition, if a solution is dominated by other solutions, the solution can generate more new solutions than those solutions, which makes the solution of each subobjective space converge to the optimal solutions as far as possible. Experimental studies have been conducted to compare this proposed algorithm with classic MOEA/D and NSGAII. Simulation results on six multiobjective benchmark functions show that the proposed algorithm is able to obtain better diversity and more evenly distributed Pareto front than the other two algorithms. 1. Introduction Since there are many problems with several optimization problems or criteria in real world [1], multiobjective optimization has become a hot research topic. Unlike single-objective optimization problem, multiobjective optimization problem has a series of noninferior alternative solutions, also known as Pareto optimal solutions (the set of Pareto optimal solutions is called Pareto front [2]), which represent the possible trade-off among various conflicting objectives. Therefore, multiobjective optimization algorithms for MOP should be able to discover solutions as close to the optimal solutions as possible; find solutions as uniform as possible in the obtained nondominated front; determine solutions to cover the true Pareto front (PF) as broad as possible. However, achieving these three goals simultaneously is still a challenge for multiobjective optimization algorithms. Among various multiobjective optimization algorithms, multiobjective evolutionary algorithms (MOEA), which make use of the strategy of the population evolutionary to optimize the problems, are an effective method for solving MOPs. In recent years, many MOEAs have been proposed for solving the multiobjective optimization problems [3–18]. In the MOEA literatures, Goldberg’s population categorization strategy [19] based on nondominance is important. Many algorithms use the strategy to assign a fitness value based on the nondominance rank of members. For example, the