The spotted Hyena Optimization Algorithm (SHOA) is a metaheuristic algorithm inspired by the hunting behavior of spotted hyenas in the wild. SHOA is based on spotted hyenas social and adaptive behavior in hunting for prey/food. The SHO encircling technique relates a “circle-shape” around the solution.
Spotted hyenas are known for their cooperative hunting behavior, working together in a pack to catch their prey. They also exhibit adaptive behavior by adjusting their hunting strategies based on the behavior of their prey and the environmental conditions.
Similarly, SHOA begins with initializing a population of candidate solutions called hyenas. Each hyena represents a potential solution to the optimization problem at hand. During the search process, the hyenas move through the search space cooperatively and adaptively, guided by their hunting behavior.
The hyena algorithm consists of two main phases:
In the hunting phase, the hyenas move through the search space in search of promising prey locations, which correspond to good solutions in the search space. The hyenas use a combination of local search and global search strategies to explore the search space.
In the feeding phase, the hyenas share their knowledge and experience to improve the overall performance of the pack. The algorithm uses a social learning mechanism to facilitate the exchange of information between the hyenas.
At the end of each iteration, the hyenas are evaluated based on a fitness function, which measures how well they perform on the optimization problem. The best hyenas are then selected to form the next generation of hyenas, while the weaker hyenas are replaced with new ones generated through random mutation.
Exploitation: The SHO algorithm aims to exploit the discovered promising solutions by focusing on the most promising regions of the search space and refining the search process to converge on the best possible solution.
Exploration: The SHO algorithm aims to explore the search space efficiently and thoroughly to find potential optimal solutions.
Adaptation: The SHO algorithm aims to adapt its search process dynamically to changing conditions or objectives during the optimization process.
Efficiency: The SHO algorithm aims to find the optimal solution to a given optimization problem in a reasonable amount of time and with minimal computational resources.
Spotted hyenas use a four-tiered pack mechanism for hunting their prey. The group mechanism is as follows:
Global optimization: SHOA is a metaheuristic optimization algorithm that is designed to find the global optimum of a given objective function. The algorithm uses a population-based search strategy that systematically and efficiently explores the search space, making it more likely to find the global optimum than traditional optimization methods.
Robustness: SHOA is a robust optimization algorithm that can handle noisy, non-linear, and non-convex functions. The algorithm uses a collective intelligence approach to adapt to changing environments and avoid getting stuck in local optima.
Flexibility: Flexible optimization algorithm that can be applied to various optimization problems and domains. The algorithm can handle different objective functions, constraints, and problem structures and be customized to suit specific problem requirements.
Easy implementation: SHOA is an easy-to-implement optimization algorithm that requires minimal parameter tuning and no prior knowledge of the problem. The algorithm uses simple rules and operations that are easy to understand and modify.
Computational complexity: SHOA is a population-based optimization algorithm that requires a large number of function evaluations to converge to the global optimum. The computational complexity of the algorithm can be a limiting factor for large-scale optimization problems or problems that require real-time solutions.
Parameter tuning: Like many other metaheuristic optimization algorithms, SHOA requires parameter tuning to perform well. The choice of parameter values can significantly impact the algorithms ability to find the global optimum, and finding optimal parameter values can be time-consuming and computationally expensive.
Limited applicability: While SHOA is a flexible optimization algorithm, it may not be suitable for all optimization problems. The algorithm performance can be affected by the problem structure, the objective function, and the presence of constraints or other problem-specific factors.
Inefficient search: The SHOA algorithm uses a random search strategy that can sometimes be inefficient or ineffective. Sometimes, the algorithm may get stuck in local optima or fail to explore the search space effectively, resulting in suboptimal solutions.
The key challenges of the Spotted Hyena Optimization Algorithm are considered as follows:
Selection of optimal parameter values: As with many other metaheuristic optimization algorithms, the performance of SHOA is sensitive to the choice of parameter values. Finding the optimal parameter values for a specific problem can be challenging and often requires significant computational resources.
Sensitivity to problem characteristics: The performance of SHOA can be highly dependent on the specific problem being solved, including the problem structure, the number of variables, and the presence of nonlinearity or other complexities. As a result, the algorithm may not be well-suited to all optimization problems.
Difficulty in handling constrained problems: SHOA is designed primarily for unconstrained optimization problems, and its performance on constrained problems is not understood. In some cases, the algorithm needs to be modified or combined with other techniques to handle constraints, which can increase the complexity of the optimization process.
Limited understanding of convergence behavior: SHOA is a stochastic optimization algorithm, and its convergence behavior is difficult to analyze theoretically. As a result, the behavior of the algorithm cannot be predicted in advance, and the optimization process may take longer than expected or converge to suboptimal solutions.
Computational complexity: SHOA requires a large number of function evaluations to converge to the global optimum. It can be computationally expensive and may limit the algorithm applicability to large-scale optimization problems.
Power system optimization:SHOA has been applied to optimize the operation of power systems, such as scheduling of power generation and distribution, to reduce the overall cost and improve system efficiency.
Load frequency control in power systems:SHOA has been applied to optimize load frequency control in power systems. By adjusting the control parameters using SHOA, the stability of the power system can be improved, and the frequency deviations can be minimized.
Heat exchanger design: SHOA has been used to optimize the design of heat exchangers in thermal systems. Optimizing the design parameters using SHOA can improve the heat transfer performance of the exchanger.
Transportation systems: SHOA has been used to optimize transportation systems, such as traffic flow management and vehicle routing, to reduce congestion, improve safety, and minimize travel time and distance.
Renewable energy systems: SHOA has been used to optimize the design and control of renewable energy systems, such as wind and solar power, to maximize energy production and improve system performance.
Water resources management: SHOA has been applied to optimize the management of water resources, such as water allocation and distribution, to improve water use efficiency and reduce water waste.
Vehicle routing problem: SHOA has been applied to solve the vehicle routing problem, an important optimization problem in logistics and transportation. Optimizing the routing and scheduling of vehicles using SHOA can improve the efficiency and cost-effectiveness of transportation systems.
