Dealing QAP & KSP with Green Heron optimization algorithm — A new bio-inspired meta-heuristic

Abstract

In this paper a new biological phenomenon following meta-heuristics called Green Heron Optimization Algorithm (GHOA) is being discussed, for the first time, which acquired its inspiration from the Green Heron birds, their intelligence, perception analysis capability and technique for food acquisition. The natural phenomenon of the bird has been capped into some unique operations which favour the graph based and discrete combinatorial optimization problems but with slight modification can also be utilized for other wide variety of problems of the real world which have discrete representation of data and variables having several constraints. In this work we have mainly concentrated on the description, mathematical representations, presentations, features, limitations and performance analysis of the algorithm on the scattered dimensional datasets of the Quadratic Assignment Problem (QAP) & 0/1 Knapsack Problem (KSP) to clearly demarcate its performance with change in dimension that is scalability. The results of the simulation clearly reveal how the algorithm has worked optimally for the various datasets of the problem. GHOA is one of the few members in the discrete domain algorithms of the bio-inspired computation family which favours suitably the graph based problems like path planning, process scheduling etc and has the capability of recombination and local search for global optimization and refinement of the solutions.