Swarm Intelligence Techniques in Artificial Economics for Energy Conservation in Enriched Cognitive Architecture

Abstract

Energy conservation is the challenging issue in cognitive architecture. Swarm intelligence techniques are an optimized method adopted in artificial economics. Enriched Cognitive architecture for conservation of energy (ECACE) is proposed and design is demonstrated. The ECACE implemented with quantum partial swarm optimization techniques in cognitive architecture simulated Testbed. The result of performance ore and crystal evaluation, The Life Expectancy of Cognition versus BDI Agents and Swarm agents and Consumption rate of Fungus and Ore Collection and life expectancy are discussed. The results obtained from ECACE are compared with Society of mind Approach for distributed cognitive architecture (SMCA) agent’s and shows that swarm agent’s performance is better than SMCA performance in energy conservation.

Keywords: Energy Conservation, Artificial Economics, cognitive architecture, swarm Intelligence, Partial Optimization algorithm, Continues quantum partial optimization algorithm, BOIDS algorithm