A compact multiagent system based on autonomy oriented computing

A compact multiagent optimization system (MAOS/sub C/) based on autonomy oriented computing (AOC) is presented. Performed by a society of autonomous entities in iterative cycles, an optimization algorithm can simply be described by a macro generate-and-test behavior, which deploys a few elemental generating behaviors under conditioned reflex behaviors supported by a testing operation library. MAOS/sub C/ provides a simple framework for not only realizing and comparing algorithms, but also deploying evolvable algorithms. The experimental results of MAOS/sub C/ cases on benchmark functions are compared with those of other algorithms, which show its efficiency.