A hybrid pigeon inspired optimization algorithm based on Nelder-Mead simplex operations

As a recent swarm-based intelligent optimization algorithm, Pigeon Inspired Optimization (PIO), is motivated by the natural bio-mechanism of pigeons for their superior skills in destination finding and navigating. The standard PIO has been successfully implemented to solve complex optimization problems. Similar to other swarm intelligent techniques, PIO is suitable to solve global optimization problems for its robustness in adapting to dynamic environments, where the convergence rate is generally limited, since the algorithm does not employ much local information to establish a most promising direction for optima searching. In this paper, we propose a novel hybridized optimization algorithm, Nelder-Mead Pigeon Inspired Optimization (NMPIO), incorporating the global optimization ability of PIO and the capability of fast local convergent of the Nelder-Mead Simplex method. With the implementation of synthesizing a bio-inspired optimization algorithm and a direct local search method, feasible global optimal solution can be found with a faster convergence rate, compared with the original PIO algorithm. Numerical experiments for several well-known benchmarks are conducted to study the performance of this algorithm. The results reveal that our hybridization strategy is effective and efficient for solving global optimization problems.