Yibing Lv

Genetic algorithm based on simplex method for solving linear-quadratic bilevel programming problem

The bilevel programming problems are useful tools for solving the hierarchy decision problems. In this paper, a genetic algorithm based on the simplex method is constructed to solve the linear-quadratic bilevel programming problem (LQBP). By use of Kuhn-Tucker conditions of the lower level programming, the LQBP is transformed into a single level programming which can be simplified to a linear programming by the chromosome according to the rule. Thus, in our proposed genetic algorithm, only the linear programming is solved by the simplex method to obtain the feasibility and fitness value of the chromosome. Finally, the feasibility of the proposed approach is demonstrated by the example.

A penalty function method based on Kuhn–Tucker condition for solving linear bilevel programming☆

Abstract Using the Kuhn–Tucker optimality condition of the lower level problem, we transform the linear bilevel programming problem into a corresponding single level programming. The complementary and slackness condition of the lower level problem is appended to the upper level objective with a penalty. Then we decompose the linear bilevel programming into a series of linear programming problems and get the optimal solution of the linear bilevel programming using linear programming method.

A neural network approach for solving linear bilevel programming problem

A novel neural network approach is proposed for solving linear bilevel programming problem. The proposed neural network is proved to be Lyapunov stable and capable of generating optimal solution to the linear bilevel programming problem. The numerical result shows that the neural network approach is feasible and efficient.

A neural network for solving a convex quadratic bilevel programming problem

A neural network is proposed for solving a convex quadratic bilevel programming problem. Based on Lyapunov and LaSalle theories, we prove strictly an important theoretical result that, for an arbitrary initial point, the trajectory of the proposed network does converge to the equilibrium, which corresponds to the optimal solution of a convex quadratic bilevel programming problem. Numerical simulation results show that the proposed neural network is feasible and efficient for a convex quadratic bilevel programming problem.

Multi-Objective Optimization of the Proposed Multi-Reservoir Operating Policy Using Improved NSPSO

Severe water shortage is unacceptable for water-supply reservoir operation. For avoiding single periods of catastrophic water shortage, this paper proposes a multi-reservoir operating policy for water supply by combining parametric rule with hedging rule. In this method, the roles of parametric rule and hedging rule can be played at the same time, which are reducing the number of decision variables and adopting an active reduction of water supply during droughts in advance. In order to maintain the diversity of the non-dominated solutions for multi-objective optimization problem and make them get closer to the optimal trade-off surfaces, the multi-population mechanism is incorporated into the non-dominated sorting particle swarm optimization (NSPSO) algorithm in this study to develop an improved NSPSO algorithm (I-NSPSO). The performance of the I-NSPSO on two benchmark test functions shows that it has a good ability in finding the Pareto optimal set. The water-supply multi-reservoir system located at Taize River basin in China is employed as a case study to verify the effect of the proposed operating policy and the efficiency of the I-NSPSO. The operation results indicate that the proposed operating policy is suitable to handle the multi-reservoir operation problem, especially for the periods of droughts. And the I-NSPSO also shows a good performance in multi-objective optimization of the proposed operating policy.

A neural network approach for solving nonlinear bilevel programming problem

A neural network model is presented for solving nonlinear bilevel programming problem, which is a NP-hard problem. The proposed neural network is proved to be Lyapunov stable and capable of generating approximal optimal solution to the nonlinear bilevel programming problem. The asymptotic properties of the neural network are analyzed and the condition for asymptotic stability, solution feasibility and solution optimality are derived. The transient behavior of the neural network is simulated and the validity of the network is verified with numerical examples.

A globally convergent algorithm for a class of bilevel nonlinear programming problem

Bilevel programming, one of the multilevel programming, is a class of optimization with hierarchical structure. This paper proposes a globally convergent algorithm for a class of bilevel nonlinear programming. In this algorithm, by use of the dual theory, the bilevel nonlinear programming is transformed into a traditional programming problem, which can be turned into a series of programming problem without constraints. So we can solve the infinite nonlinear programming in parallelism to obtain the globally convergent solution of the original bilevel nonlinear programming. And the example illustrates the feasibility and efficiency of the proposed algorithm.

A penalty function method for solving weak price control problem

Abstract The price control problem is an important linear bilevel programming problem. In this paper, we are concerned with a class of weak price control problems with non-unique lower level solutions. For such problems, we study the existence of solution via a penalty method. Then, we propose a simple algorithm for this problem, and present the convergence of the algorithm. The example illustrates that the method is feasible and efficient.

A penalty function method based on bilevel programming for solving inverse optimal value problems

In this work, we reformulate the inverse optimal value problem equivalently as a corresponding nonlinear bilevel programming (BLP) problem. For the nonlinear BLP problem, the duality gap of the lower level problem is appended to the upper level objective with a penalty, and then a penalized problem is obtained. On the basis of the concept of partial calmness, we prove that the penalty function is exact. Then, an algorithm is proposed and an inverse optimal value problem is resolved to illustrate the algorithm.

Operating rules classification system of water supply reservoir based on Learning Classifier System

Genetic algorithm-based learning classifier system (LCS) is a massively parallel, message-passing and rule-based machine learning system. But its potential self-adaptive learning capability has not been paid enough attention in reservoir operation research. In this paper, an operating rule classification system based on LCS, which learns through credit assignment (the bucket brigade algorithm) and rule discovery(the genetic algorithm), is established to extract water-supply reservoir operating rules. The proposed system acquires the online identification rate 95% for training samples and offline rate 85% for testing samples in a case study, and further discussions are made about the impacts on the performances or behaviors of the rule classification system from three aspects of obtained rules, training or testing samples and the comparisons between the rule classification system and the artificial neural network (ANN). The results indicate the learning classifier system is practical and effective to obtain the reservoir supply operating rules.