Tiesong Hu

An improved particle swarm optimization algorithm

Because the variable inertia weight particle swarm optimization algorithm is easy to fall into the local optimum, this paper introduces the improved simulated annealing operator, chaotic disturbance operator and Cauchy mutation operator to the former and proposes an improved particle swarm optimization algorithm; Then, two typical Benchmark functions are used to test the performance of basic the proposed algorithm; Finally, the relations of population size and particle dimension to performance of the proposed algorithm is analyzed. Simulation results show that while maintains the superiorities of simple structure, few parameters and the ease of implement, the proposed algorithm improves the convergence precision largely.

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.

Extension of Parametric Rule with the Hedging Rule for Managing Multireservoir System during Droughts

In contrast to most common methods used in optimal control of reservoir systems requiring a large number of decision variables, parametric rule can make a radical reduction in the number of decision variables without yielding inferior solutions. However, parametric rule employs the standard operating policy to determine releases of reservoirs as much as demand only if there is enough water in the system, which may result in single periods of severe short supply during droughts. The purpose of this paper is to devise an operating rule for multireservoir system by combining parametric rule with the hedging rule to avoid catastrophic water shortage during droughts. In this way, decision variables to be optimized not only make a significant reduction compared with traditional operating rules, but also severe short supply during droughts can be controlled effectively. This paper employs a water supply multireservoir system in northern China to explore the changes of shortage characteristics produced by the proposed rule over a long horizon. In the case study, particle swarm optimization algorithms with a simulation model are used to optimize the decision variables. The results indicate that the extended parametric rule has a significant advantage over the classic parametric rule in dealing with the multireservoir operation problem during droughts. DOI: 10.1061/(ASCE)WR.1943-5452.0000241.

Solving high dimensional bilevel multiobjective programming problem using a hybrid particle swarm optimization algorithm with crossover operator

In this paper, a hybrid particle swarm optimization with crossover operator (denoted as C-PSO) is proposed, in which a crossover operator is adopted for enhancing the information exchange between particles to prevent premature convergence of the swarm. The C-PSO algorithm is employed for solving high dimensional bilevel multiobjective programming problem (HDBLMPP) in this study, which performs better than the existing method with respect to the generational distance and has almost the same performance with respect to the spacing. Finally, we use four test problems and a practical application to measure and evaluate the proposed algorithm. Our results indicate that the proposed algorithm is highly competitive with respect to the algorithm representative of the state-of-the-art in high dimensional bilevel multiobjective optimization.

An Improved Particle Swarm Optimization for Solving Bilevel Multiobjective Programming Problem

An improved particle swarm optimization (PSO) algorithm is proposed for solving bilevel multiobjective programming problem (BLMPP). For such problems, the proposed algorithm directly simulates the decision process of bilevel programming, which is different from most traditional algorithms designed for specific versions or based on specific assumptions. The BLMPP is transformed to solve multiobjective optimization problems in the upper level and the lower level interactively by an improved PSO. And a set of approximate Pareto optimal solutions for BLMPP is obtained using the elite strategy. This interactive procedure is repeated until the accurate Pareto optimal solutions of the original problem are found. Finally, some numerical examples are given to illustrate the feasibility of the proposed algorithm.

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.

Water Transfer Triggering Mechanism for Multi-Reservoir Operation in Inter-Basin Water Transfer-Supply Project

This paper proposes a new water transfer triggering mechanism for multi-reservoir system to divert water from abundant to scarce regions with a constant diversion flow in an inter-basin water transfer-supply project. Taking into account of the uncertain nature of inflow, the storage of reservoir is taken as a signal for decision-making to indicate water abundance or water scarcity. In this study, a set of rule curves based on storage of donor reservoir and storage of recipient reservoir are used together to determine when to start water transfer. To initiate water diversion to each recipient reservoir effectively, several water transfer rule curves of the donor reservoir are set for each recipient reservoir respectively in the multi-reservoir system with one donor reservoir and several recipient reservoirs, which is the main difference in comparison with other water transfer triggering mechanisms. In addition, a systematic framework is developed to integrate the water transfer rule curves with hedging rule curves to simultaneously solve the water transfer and water supply problems, since they interact with each other during the operation process. In order to verify the utility of the new water transfer triggering mechanism, an inter-basin water transfer-supply project in China is used as a case study and an improved particle swarm optimization algorithm (IPSO) with a simulation model is adopted for optimizing the decision variables. The results show that the proposed water transfer triggering mechanism can improve the operation performances of the inter-basin system.

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.