Search

In this paper, we treat optimization problems as a kind of reinforcement learning problems regarding an optimization procedure for searching an optimal solution as a reinforcement learning procedure for finding the best policy to maximize the expected rewards. This viewpoint motivated us to propose a Q-learning-based swarm optimization (QSO) algorithm. The proposed QSO algorithm is a population-based optimization algorithm which integrates the essential properties of Q-learning and particle swarm optimization. The optimization procedure of the QSO algorithm proceeds as each individual imitates the behavior of the global best one in the swarm. The best individual is chosen based on its accumulated performance instead of its momentary performance at each evaluation. Two data sets including a set of benchmark functions and a real-world problem-the economic dispatch (ED) problem for power systems-were used to test the performance of the proposed QSO algorithm. The simulation results on the benchmark functions show that the proposed QSO algorithm is comparable to or even outperforms several existing optimization algorithms. As for the ED problem, the proposed QSO algorithm has found solutions better than all previously found solutions. [ABSTRACT FROM AUTHOR]

Abstract: In most hospitals, operating rooms are the most costly facilities and consume a large percentage of the hospital recourses. Therefore, an efficient and effective operating room scheduling system is highly demanded for hospitals. In this paper, a SOMO-based approach to solving the operating room scheduling problem is proposed. Computational experiments performed on 100 randomly generated simulations are conducted to test whether the proposed scheduling algorithm can provide appealing arrangements in a reasonable computation time. [Copyright &y& Elsevier]

The conventional self-organizing feature map (SOM) algorithm is usually interpreted as a computational model, which can capture main features of computational maps in the brain. In this paper, we present a variant of the SOM algorithm called the SOM-based optimization (SOMO) algorithm. The development of the SOMO algorithm was motivated by exploring the possibility of applying the SOM algorithm in continuous optimization problems. Through the self-organizing process, good solutions to an optimization problem can be simultaneously explored and exploited by the SOMO algorithm. In our opinion, the SOMO algorithm not only can be regarded as a biologically inspired computational model but also may be regarded as a new approach to a model of social influence and social learning. Several simulations are used to illustrate the effectiveness of the proposed optimization algorithm. [ABSTRACT FROM AUTHOR]

Abstract: Optimization of construction time helps practitioners save time and money. In this study we compare two optimization methods — self organizing map based optimization (SOMO) and particle swarm optimization (PSO) — for minimizing the construction time of a secant pile wall. The comparison is based on data from 207 primary and secondary bored piles for a secant pile wall. The detailed construction time is measured in minutes and broken down into 16 work activities for each unit of the wall which is then used to yield the optimal construction sequence and time. Both optimization methods can be computed quickly but, SOMO yields a more efficient construction sequence with a shorter construction time. The comparison leads to several findings. Particles with randomly selected velocities and positions may lead to convergence at a local optimization. The PSO comparison mechanism for the yielding of optimization limits the process to the winner''s neighbors, which may also converge to a local optimization. The case study shows the superiority of SOMO and provides answers to the dilemmas using the smallest hyper-plane, the entire comparison mechanism to yield the optimization, and the mechanism for weight adjustment between the winner neuron and its neighbors. [Copyright &y& Elsevier]