Tree-seed algorithm (TSA) is a nature-inspired metaheuristics algorithm developed based on the natural phenomenon of tree propagation. This algorithm is inspired by the natural cycle between trees and seeds in nature and how tree seeds grow and are flexible in their position.
TSA is extensively utilized in the field of heuristic and population-based search, considered to have improved the original defects of the optimization problems, that is, the inverse correlation between exploration and exploitation in the searching process. It has been proposed for very low-dimensional optimization problems and achieved promising results compared to other optimization algorithms.
TSA cannot scan the local optimum and search space effectively. It is a convenient algorithm used to solve a continuous optimization problem, which is applied in many fields because of its simplicity and strength in finding optimal solutions.
TSA has such an imbalance in its ability between exploration and exploitation in different search phases, so the exploratory capability of TSA is relatively weak in optimizing multimodal and high dimensional objective functions.
Effective exploration and exploitation: TSA uses germination and growth phases to explore and exploit the search space effectively. The germination phase generates diverse candidate solutions, while the growth phase combines the best solutions to generate new ones. This balance between exploration and exploitation helps TSA to escape local optima and converge faster to the global optimum.
High performance: TSA has shown promising results in various optimization problems, outperforming other popular metaheuristic algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO). TSAs ability to converge quickly to the global optimum and handle large-scale problems makes it a suitable candidate for many real-world applications.
Scalability: TSA is highly scalable and can handle large-scale optimization problems with many variables and constraints. TSA uses a population-based approach to explore the search space efficiently and find the optimal solution in a reasonable time.
Robustness: TSA is robust to noisy or incomplete data and can handle multimodal and non-convex optimization problems. TSA population-based approach enables it to find multiple solutions simultaneously, which can help to overcome the problem of getting stuck in local optima.
Flexibility: TSA can be customized to suit different optimization problems and objectives. The algorithms growth rules can be adapted to match the problem structure, and the germination phase can use different mutation, crossover, or local search operators to modify the candidate solutions.
Easy implementation: TSA is relatively easy to implement compared to other metaheuristic algorithms. The algorithms basic operations are simple, and the implementation requires only a few parameters to be tuned.
Parameter tuning: TSA has several parameters that need to be tuned to achieve optimal performance; selecting the appropriate values for the population size, growth rules, mutation rate, and termination criteria can be challenging and time-consuming.
Convergence speed: TSA is generally fast at converging a solution. It may not always be as fast as other algorithms in some situations. For example, for some problems, algorithms like Simulated Annealing can converge faster to the global optimum.
Complexity: TSAs growth phase can be computationally expensive, especially for high-dimensional problems or when using complex growth rules. The algorithm performance may also depend on the quality of an initial population of seeds and the order in which they are grown.
Sensitivity to problem structure: It can be affected by the structure of the optimization problem. For example, if the problem is highly constrained or has many local optima, TSA may struggle to find the global optimum.
Lack of standardization: TSA has no standardized implementation or benchmarking framework. This makes it challenging to compare its performance with other optimization algorithms and may lead to inconsistent results across different studies.
Local optima: TSA may get stuck in local optima and fail to find the global optimum. It is a significant challenge, especially for multimodal optimization problems or problems with a complex fitness landscape.
Complexity: TSA growth phase can be computationally expensive, especially for high-dimensional problems or when using complex growth rules. It results in slow convergence, and scaling the algorithm to handle large-scale optimization problems may be challenging.
Lack of empirical studies: Although TSA has shown promising results in some optimization problems, there is still a lack of empirical studies that validate its performance and compare it with other optimization algorithms on a wide range of problems.
Sensitivity to problem structure: TSA performance can be affected by the structure of the optimization problem. For example, if the problem is highly constrained or has many local optima.
TSA has shown promising results in various optimization problems, including but not limited to:
