Research Of Swarm Intelligence In Optimization And Simulation

Swarm intelligence (SI) is artificial intelligence based on the collective behavior of decentralized, self-organized systems. It is inspired by nature swarm behaviors. The examples of nature SI include ant colonies, bird flocking, animal herding, bacterial growth, and fish schooling. The expression was introduced by Gerardo Beni and Jing Wang in 1989, in the context of cellular robotic systems. The traditional"up-down"methods encounter lots of handicaps in the research of complex systems. A new method, swarm intelligence which is a"bottom-top"research method, is exploited widely in optimization and simulation of complex systems now. Swarm intelligence technology is particularly attractive because it is simple, effective and robust. Swarm intelligence focuses on self-organized, decentralized methods. The swarm systems typically are made up of population of simple agents which interact with each other and environment. The macrostructures emerge from the interactions and operations of agents despite the simple micro rules.There are many optimization models based on SI, and particle swarm optimization algorithm is a typical one. It is inspired by the social behavior of bird flocking. In this algorithm, every particle will modify its search direction and speed according to the best swarm solution and its own best solution. Following this rule, the swarm can search the solution space effectively. The micro-structure of PSO algorithm is simple, but the macro searching behavior is emerging from it based on inter-operations of particles.The traditional macro models cannot explain many social phenomena clearly. In such system, there are lot of agents which have interactions and inter-influences, e.g. traffic systems and economic systems. The micro-models based on swarm intelligence approach have made great improvements in recent years. Most of these models focus on sociology and economics. The agent-based computational economics is a classical one. SI presents a different perspective from micro level. Swarm intelligence techniques have lots of advantages to simulate these systems. In simulations of economic systems, swarm intelligence is used to study and validate economic hypotheses that are hard for traditional approaches.The work of this dissertation focuses on optimization and simulation applying SI approaches. There are two important issues in the SI optimization model: How to map agents to solution space? How agents cooperate to improve the searching efficiency.The two main parts of the work on optimization are listed below:1) A new PSO algorithm is presented for solving travel salesman problem. Swap operator is presented as"speed"to change the current solution to a new one. The distance of two solutions can also be denoted as"swap operator". In this way, particle swarm optimization can be used in discrete domain.2) An agent-based algorithm is presented for solving multi-objective optimization problems. In multi-objective problems, the key is how to mediate different objectives. Agents with different intents are used to search the solution space. Agents can exchange information with their neighbors in the searching process to improve the local searching efficiency. One evolutional operator is also employed. The experimental results show this method is effective.In simulation area, the following subjects are studied:1) We research CDA market. Some well-known strategies are implemented to study their performances.2) Agent-based simulation on pricing in the barter market. The continuous double auction model in which the supply and demand are fixed is used to explore the pricing process of agents. In this dissertation, a novel and interesting agent model is proposed to explore some properties of a typical and meaningful phenomenon in economics, pricing.In our model, not only auction is included, but also production. We analyse the pricing procedure. We find the price is stable and the variance of price is small. There are some macro phenomena emerging from this model, e.g. the business man, the social labor allocation. And then the impact of transaction capability is researched.3) Traffic signal simulation and research. A traffic system is simulated on cellular automata. Because of the complexity of environment, the traffic information always contains some noises. In this model, some traffic signal methods are tested with noisy traffic information. We find the complicated intelligent strategies are worse when there are noises in traffic information. The light control methods depend on the road structure and the traffic density severely. Traffic light synchronization method is studied on double ring road structure with five different densities. With one of the densities, the traffic light synchronization method has some problems. An improved method is presented to handle them, and the results are promising.In the end, the summaries and further works are presented.