Artificial Plant Root System Growth for Distributed Optimization: Models and Emergent Behaviors

Abstract

Open Life Sci. 2016; 11: 447-457 an optimal solution and the natural process of foraging for food [1,2]. These bio-inspired computing approaches are increasingly used by engineers and scientists to solve complex optimization problems that are intractable using conventional methods [3]. Generally, a bio-inspired optimization problem solving process occurs in the following manner: an initial position is randomized in the search space, and its acceptability assessed through the application of a fitness function; a bio-inspired position change strategy, consistent with the paradigm in use, is then iteratively applied with the hope of improving the solution acceptability; the final solution is identified either through achieving an acceptable level of fitness or on the completion of a set amount of computation.Successful examples of such bio-inspired computing algorithms as evolutionary algorithms [4] include genetic algorithm, evolutionary strategy, evolutionary programming, and genetic programming, ant colony systems [5], particle swarm optimization [6,7], and bee foraging algorithms [8].Although foraging behavior is a typically considered an animal characteristic, other organisms, including plants, have shown similar traits [9]. Because of plants' unique non-motile way of life, they only have access to resources nearby their growth site [10]. The above description is the main difference between plant growth and animal foraging. Obviously, the survival rules for each plant species is to efficiently find soil with sufficient nutrients and water. So, the ability of plant roots to sense they myriad factors in their local environment allows them to complete in the evolution process, and the growth direction and root system development are driven by these factors [11].Continuous changes of the natural environment are considered as the reason of plant root growth diversity, including increased lateral branching, root biomass, root length and uptake capacity. It should be noted that these developmental needs require correct auxin transport and signaling [12]. The number of roots and the length per unit mass of roots also changes in to response to heterogeneity [9]. Many studies have demonstrated that plant foraging is DOI 10.1515DOI 10. /biol-2016 Received May 14, 2016; accepted September 14, 2016 Abstract: Plant root foraging exhibits complex behaviors analogous to those of animals, including the adaptability to continuous changes in soil environments. In this work, we adapt the optimality principles in the study of plant root foraging behavior to create one possible bio-inspired optimization framework for solving complex engineering problems. This provides us with novel models of plant root foraging behavior and with new methods for global optimization. This framework is instantiated as a new search paradigm, which combines the root tip growth, branching, random walk, and death. We perform a comprehensive simulation to demonstrate that the proposed model accurately reflects the characteristics of natural plant roo...