A cooperative coevolution PSO technique for complex bilevel programming problems and application to watershed water trading decision making problems

Abstract

The complex bilevel programming problem (CBLP) in this paper mainly refers to the optimistic BLP in which the highdimensional decision variables at both levels. A cooperative coevolutionary particle swarm optimization (CCPSO) is proposed for solving the (CBLP), in which the evolutionary paradigm can efficiently prevent the premature convergence of the swarm. Furthermore, the stagnation detection strategy in our algorithm can further accelerate the convergence speed. Finally, we use the test problems from the reference and practical example about watershed water trading decision-making problem to measure and evaluate the proposed algorithm. The presented results indicate that the proposed algorithm can effectively solve the complex bilevel programming problems. c 2017 All rights reserved.Keywords: Complex bilevel programming, cooperative coevolutionary particle swarm optimization, watershed water trading decision making problems, elite strategy. 2010 MSC: 65K10, 90C25.

The Backpropagation Artificial Neural Network Based on Elite Particle Swam Optimization Algorithm for Stochastic Linear Bilevel Programming Problem

“…In our algorithm, for the updated upper level decision variable, we can predict the corresponding lower level optimal solution directly by BPANN. From the above analysis, we can see that the computational efficiency of our proposed algorithm performs well than the method in the reference [52]. Furthermore, we also give the basic working framework for these two algorithms (see Figure 2) Algorithm 5.…”

“…We proposed the BPANN-PSO for problem (11) and we update the upper level decision variables using the method in [52]. However, the way to solve the lower level problems is completely different.…”

“…However, the way to solve the lower level problems is completely different. In reference [52], for each updated upper level decision variable, we need to reexecute the lower level optimization problem by cooperative coevolution PSO (CCPSO). In other words, the historical optimal solutions do not contribute to the current optimal solution.…”

“…Initialization upper level decision variableSolve the lower level problem by CCPSO in Reference[52] Predict the lower level optimal solution by BPASS-The basic working framework for two algorithms.…”

A Solving Algorithm for Nonlinear Bilevel Programing Problems Based on Human Evolutionary Model

“…For the second category, there are no limitations on the differentiability of the functions, so many researchers tend to develop heuristic algorithms for solving BLPP, including: the genetic algorithm (GA) [15][16][17][18], the state transition algorithm (STA) [9], particle swarm optimization (PSO) [19][20][21], tabu search (TS) [22], multi-sine cosine algorithm [23], artificial neural network (ANN) [16,[24][25][26], homotopy method [27], evolutionary algorithm (EA) [28][29][30], simulated annealing (SA) [17], artificial bee colony algorithm (ABC) [31], ant colony algorithm (AC) [32], etc.…”