Co-evolutionary Particle Swarm Optimization Algorithm Research Articles

Flow Shop Production Scheduling under Uncertainty within Infinite Intermediate Storage

As to current situation of research on production scheduling under uncertainties in enterprises, Flow Shop production scheduling model is established based on the theory of fuzzy programming, in which fuzzy processing time is considered and the duration time of intermediate is unlimited. The maximum membership function of mean value has been applied to solve the non-linear fuzzy scheduling model in order to convert the fuzzy optimization problem to the general optimization problem. Finally, a cooperative co-evolutionary particle swarm optimization algorithm based on catastrophe added to improve the diversity of the swarm (CCPSO) is adopted to solve Flow Shop Production Scheduling Under Uncertainty within Infinite Intermediate Storage and results are obtained effectively.

A Cooperative Co-evolution PSO for Flow Shop Scheduling Problem with Uncertainty

Considering current situation of production scheduling with uncertainties in modern manufacturing enviroments, flow shop production scheduling model is established based on the theory of fuzzy programming, in which fuzzy processing time is considered and the duration time of intermediate is unlimited. The maximum membership function of mean value has been applied to solve the non-linear fuzzy scheduling model in order to convert the fuzzy optimization problem to the general optimization problem. Finally, a cooperative co-evolutionary particle swarm optimization algorithm based on catastrophe added to improve the diversity of the swarm (CCPSO) is adopted to solve flow shop production scheduling with uncertainty within infinite intermediate storage and the simulation results obtained are effective and satisfactory.

A Robust Cooperative Coevolutionary Particle Swarm Optimization Algorithm for Triangulation of Bayesian Networks

In this paper, a cooperative coevoluationary particle swarm optimization algorithm, CCMDPSO, is proposed to solve the optimization problem of triangulation of Bayesian networks. It arranges all the variables of a given Bayesian network into some groups according to the global best solution and performs optimization on these small-scale groups. The basic optimizer of CCMDPSO is an improved discrete particle swarm optimization algorithm, MDPSO. Experiments show that CCMDPSO is an effective and robust method for the triangulation problem.

A Hybrid Co-evolutionary Particle Swarm Optimization Algorithm for Solving Constrained Engineering Design Problems

This paper presents an effective hybrid co-evolutionary particle swarm optimization algorithm for solving constrained engineering design problems, which is based on simulated annealing (SA) , employing the notion of co-evolution to adapt penalty factors. By employing the SA-based selection for the best position of particles and swarms when updating the velocity in co-evolutionary particle swarm optimization algorithm . Simulation results based on well-known constrained engineering design problems demonstrate the effectiveness, efficiency and robustness on initial populations of the proposed, and can reach a high precision .

Hybrid co-evolutionary particle swarm optimization and noising metaheuristics for the delay constrained least cost path problem

This paper presents a co-evolutionary particle swarm optimization (PSO) algorithm, hybridized with noising metaheuristics, for solving the delay constrained least cost (DCLC) path problem, i.e., shortest-path problem with a delay constraint on the total "cost" of the optimal path. The proposed algorithm uses the principle of Lagrange relaxation based aggregated cost. It essentially consists of two concurrent PSOs for solving the resulting minimization-maximization problem. The main PSO is designed as a hybrid PSO-noising metaheuristics algorithm for efficient global search to solve the minimization part of the DCLC-Lagrangian relaxation by finding multiple shortest paths between a source-destination pair. The auxiliary/second PSO is a co-evolutionary PSO to obtain the optimal Lagrangian multiplier for solving the maximization part of the Lagrangian relaxation problem. For the main PSO, a novel heuristics-based path encoding/decoding scheme has been devised for representation of network paths as particles. The simulation results on several networks with random topologies illustrate the efficiency of the proposed hybrid algorithm for the constrained shortest path computation problems.

Eliciting compact T–S fuzzy models using subtractive clustering and coevolutionary particle swarm optimization

This paper presents a two-stage method to extract a compact Takagi–Sugeno (T–S) fuzzy model using subtractive clustering and coevolutionary particle swarm optimization (CPSO) from data. On the first stage, the subtractive clustering is utilized to partition the input space and extract a set of fuzzy rules. On the second stage, CPSO algorithm is used to find the optimal membership functions (MFs) and consequent parameters of the rule base. Simulation results on the benchmark modeling problems show that the proposed two-stage method is effective in finding compact and accurate T–S fuzzy models.

Co-evolutionary particle swarm optimization to solve constrained optimization problems

This paper presents a co-evolutionary particle swarm optimization (CPSO) algorithm to solve global nonlinear optimization problems. A new co-evolutionary PSO (CPSO) is constructed. In the algorithm, a deterministic selection strategy is proposed to ensure the diversity of population. Meanwhile, based on the theory of extrapolation, the induction of evolving direction is enhanced by adding a co-evolutionary strategy, in which the particles make full use of the information each other by using gene-adjusting and adaptive focus-varied tuning operator. Infeasible degree selection mechanism is used to handle the constraints. A new selection criterion is adopted as tournament rules to select individuals. Also, the infeasible solution is properly accepted as the feasible solution based on a defined threshold of the infeasible degree. This diversity mechanism is helpful to guide the search direction towards the feasible region. Our approach was tested on six problems commonly used in the literature. The results obtained are repeatedly closer to the true optimum solution than the other techniques.