ABSTRACT
This chapter explains larger systems—molecules and clusters, i.e., systems that evoke strong chemical interest. The algorithm displays the self-organization ability of the swarm and performs extremely well for clusters dominated by long-range interactions. The analysis of the many-dimensional potential energy surface (PES) in chemistry focuses on ascertaining whether a stationary point located is a stable and true minimum, or whether it can be characterized as a first-order saddle point. The foregoing results of applications of the completely adaptive random mutation hill climbing (CARMHC) procedure to a system of point charges confined in a three-dimensional harmonic potential with axial anisotropy reveal that a simple soft computing algorithm. The evolutionary computing techniques and their hybrids have registered impressive success in the elucidation of minimum energy structures of atomic clusters and crystalline solids. The impressive success of artificial bee colony (ABC) in these fields has stimulated interest in exploring ABC in global optimization problems in chemistry and physics.
