ABC Metaheuristic Based Optimized Adaptation Planning Logic for Decision Making Intelligent Agents in Self Adaptive Software System

Abstract

The potential of machine intelligence is enormously increasing with a vision of computing systems that can act as good decision making and self managing entities. This led to the introduction of systems that are more intelligent with self* properties and are known as Self Adaptive Software Systems (SAS). Intelligent Agents which has a high adaptation capability forms the main component of such systems. These self adaptive systems are provided with the ability of self–configuring based on the run time environmental changes which guarantee the overall system functional and QoS goals. This paper proposes an optimized decentralized adaptation logic for modeling SAS which exploits the multi-agent concept. Each subsystem has an objective and uses an Artificial Bee Colony metaheuristic to achieve local optimization which in turn leads to the optimization of the whole distributed system.