Fostering Positive Interactions Through Psychological Ownership in Intelligent Systems

Swarm intelligence systems are basically made up simple agent's populations which are interacting locally with each other and with their surroundings. These agents local interaction with each other can be negative, positive or neutral. Here positive interaction helps agents to solve a problem while negative interaction block the agents for solving problem. swarm's performance does not affected by neutral interaction. This work proposed incremental enhanced ABC algorithm with local search which is used for reducing negative interaction without complexifying the agent's behavior. Here in the enhanced ABC algorithm, after the scout bee phase, one supplementary phase in the form of mutation operator, which is the part of genetic algorithm, is used. With the use of this supplementary phase, an algorithm may not be stopped into local optima because of changing local best position. The experimental outcomes show that the efficiency of proposed algorithm. Finally the proposed algorithm is compared with novel ABC algorithm, ABCM (artificial bee colony with mutation) algorithm.