Self-adaptive root growth model for constrained multi-objective optimization

This paper presents a general optimization model gleaned ideas from plant root growth behaviors in the soil. The purpose of the study is to investigate a novel biologically inspired methodology for complex system modelling and computation, particularly for constrained multi-objective optimization. A novel method called “multi-objective root growth algorithm” (MORGA) for constrained multi-objective optimization is proposed based on the root growth model. A self-adaptive strategy is adopted to tie this model closer to plant root growth behaviors in nature, as well as improve the robustness of MORGA. Simulation experiments of MORGA on a set of benchmark test functions are compared with other nature inspired techniques for multi-objective optimization which includes nondominated sorting genetic algorithm II (NSGA II) and multi-objective particle swarm optimization (MOPSO). The numerical results demonstrate MORGA approach is a powerful search and optimization technique for constrained multi-objective optimization.