Smart Root Search (SRS): A New Search Algorithm to Investigate Combinatorial Problems

In recent years researchers have tried to apply Stochastic Algorithms for solving Optimization problems. Some of these algorithms like Genetic Algorithm (GA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO) and Artificial Immune Systems (AIS) are more known because of their significant abilities in finding optimal solutions of the problems comparing to others. Although these algorithms show many advantages in solving optimization problems, they face some drawbacks affect on their performance. Local optima issues and lack of local search capability are two obvious weaknesses that each of the algorithms confronts at least one. To cope these challenges, in this study, plants' root growth intelligences will be inspired in proposing a novel nature-inspired optimization algorithm called Smart Root Search (SRS). The SRS simulates the extracted intelligent behaviors of plant roots in finding nutrition and water in soil. The proposed algorithm will provide embedded exploitation mechanisms besides quick and effective exploring features for escaping of the trap of local optima, and uses problem-space division. Using partitioned problem search space not only enhances ability of the algorithm to avoid of local optima, but makes it a scalable and flexible method in performing balanced search activities. The algorithm will further demonstrate its high-performance search ability in confronting high-dimensional search-space problems.