Keiichiro Yasuda

Adaptive particle swarm optimization

The particle swarm optimization (PSO) method is one of the most powerful methods for solving unconstrained and constrained global optimization problems. Little is, however, known about how the PSO method works or finds a globally optimal solution of a global optimization problem when the method is applied to global optimization problems. This paper deals with the analysis of the dynamics of PSO in order to obtain an understanding about how it searches a globally optimal solution and a strategy about how to tune its parameters. While a generalized reduced model of PSO is proposed in order to analyze the dynamics of PSO, the stability analysis is carried out on the basis of both the eigenvalue analysis and some numerical simulations on a typical global optimization problem.

Proximate optimality principle based Tabu Search

This paper proposes an algorithm-Multi Criteria Tabu Search coordinating the intensification and the diversification based on Proximate Optimality Principle (POP)-which has several advantages for solving combinatorial optimization problems. The proposed algorithm is applied to some traveling salesman problems which are typical combinatorial optimization problems in order to verify the performance of the proposed algorithm. The simulation results indicate that the proposed method has higher optimality than the conventional Tabu Search.

Decentralized autonomous control of super distributed energy systems

This paper focuses on a super distributed energy system that consists of a number of dispersed generation systems such as fuel cells, micro gas-turbines, and so on. The behavior of a customer with a dispersed generation system is modeled on the Ising spin model in statistical mechanics. The validity of the modeling of the behavior of a customer is verified based on the Monte Carlo simulation. The feasibility of the decentralized autonomous control using the vicinity information is also investigated on the basis of the stability analysis of the Hopfield neural network model.

Multi-trajectory dynamic tunneling algorithm

AbsfmciThis paper proposes a new dynamic tunneling algorithm with multi-trajectories (Multi-Trajectory Dynamic Tunneling Algorithm) that is composed of two systems, an Optimization system and a tunneling system. These systems are used sequentially to approach a global optimal 8olutlon of an objective function. The numerical stability of the conventional dynamic tunneling algorithm is theoretically investigated and the interaction between each trajectory of the tunneling system is Introduced in order to improve search efficiency. The proposed algorithm is applied to %variable and IO-veriable typical multi-peaked nonlinear optimization problems. K q w o n i Global optimization, tunneling algorithm, dynamic optimization, rneta-heuristics.

Analysis of the dynamics of particle Swarm optimization

Abstract The Particle Swarm Optimization (PSO) method is one of the most powerful methods for solving unconstrained and constrained global optimization problems. Little is, however, known about how the PSO method works or finds a globally optimal solution of a global optimization problem when the method is applied to global optimization problems. This paper deals with the analysis of the dynamics of PSO in order to obtain an understanding about how it searches a globally optimal solution and a strategy about how to tune its parameters. While a generalized reduced model of PSO is proposed in order to analyze the dynamics of PSO, the stability analysis is carried out on the basis of both the eigenvalue analysis and Bounded Input Bounded Output (BIBO) theory in control engineering. Some numerical simulations on typical global optimization problems are also carried out and discussed.