Development of a Higher Order Cognitive Optimization algorithm - Tanweer, M. R.; Suresh, S.; Sundararajan, N. (2017 IEEE Congress on Evolutionary Computation (CEC), 2017)
In this paper, we develop a human social intelligence inspired population-based optimization algorithm called Higher Order Cognitive Optimization (HOCO) algorithm. Each of the individuals in this HOCO possess human-like characteristics such as decision making ability, self/social-awareness, self/social belief, shared information processing, and self-regulation. These characteristics are modeled as a hierarchical inter-related structure with each layer realizing different levels of granularity. In this paper, HOCO is implemented as a three layered inter-related architecture for single-objective optimization. The main aspects of the proposed optimization technique are: (1) development of a socially intelligent optimization algorithm; (2) each individual employs their meta-cognitive as well as social meta-cognitive abilities, in addition to the cognitive abilities to attain the global optimal solution; and (3) the meta-cognitive and social meta-cognitive components self-regulate the cognitive component by adapting its strategies, such that a globally optimal solution formulation is achieved. Performance has been analyzed on six standard benchmark problems and compared with other meta-heuristic algorithms. Further, the performance on computationally expensive CEC2015 benchmark problems has also been studied. The comparison with other population based meta-heuristic approaches indicates the significance of the HOCO algorithm.