African Vultures Optimization Algorithm (AVOA) is a new population-based metaheuristic algorithm greatly inspired by the intelligence, navigation, and collective foraging behaviour of creature key skills in nature for acquiring food/prey eating and movement patterns for authentic to maintain and lead their livelihood.
AVOA is used to solve continuous applications and enhanced many strategies that has been extensively utilized in large scale optimization engineering applications as complex, continuous and discrete ones. It merely helps in improving the imbalance amid between exploitation and exploration ability, lack of crowd diversity, trapping in local optima, less convergence speed, and early convergence rate of AVOA.
AVOA manifest the superiority to expose the applicability which is employed to find the nearest best optimal solutions for optimal engineering design problems. AVOA is the best and most promising algorithm method used for solving a variety of complex engineering designs.
Feeding behavior:African vultures are scavengers and have developed a feeding behavior which allows them to efficiently locate and consume carrion. This algorithm uses this principle to guide the search process towards promising solutions.
Flight behavior:African vultures use keen eyesight and soaring ability to locate and track prey over long distances. The AVOA algorithm uses this principle to guide the search process towards promising regions of the search space by simulating the flight behavior of vultures.
Group behavior:African vultures are social animals, often feed in groups to encourage exploration of the search space by allowing multiple search agents to work together to find good/better solutions.
Initialize the population:Start by generating an initial population of solutions. The solutions can be randomly generated or initialized using other methods, such as Latin hypercube sampling.
Calculate the fitness of each solution:Evaluate the fitness of each solution using the objective function. It should be a measure of the quality of the solution, and should be designed to be minimized or maximized depending on the problem.
Group formation:Dividing the population into groups of African vultures. The group size can be variable or fixed, depending on the problem.
Foraging behavior:For each group, simulate the foraging behavior of African vultures. This involves searching the search space for food. The search behavior can be guided by various factors, such as the proximity of other groups, the quality of nearby solutions, and the need to explore new areas of the search space.
Feeding behavior:Once a food item (a good solution) is found, the group members engage in feeding behavior to involves sharing information about the food item and updating their search behavior based on the quality of the food item.
Update the population:After the feeding behavior, the solutions are updated based on the quality of the food item. The population is then re-evaluated using the objective function, and the process repeats from step 3 until a stopping criterion is met based on its needs.
Stopping criterion: The algorithm stops/breaks when a stopping criterion is met. This can be a maximum number of iterations, a threshold for the fitness value, or other criteria.
Output the best solution:This algorithm outputs the best solution found during the optimization process. This is the solution with the lowest or highest fitness value, depending on the given problem.
The AVOA has several merits that make it an effective optimization algorithm for a wide range of applications. Some of the main advantages of the AVOA algorithm are:
Effective exploration of the search space:The AVOA algorithm uses the group behavior of African vultures to encourage exploration of the search space. This means that the algorithm is able to efficiently search for good solutions even in large and complex search spaces.
Flexibility:AVOA algorithm is a general-purpose optimization algorithm that can be applied to a vast range of optimization problems to makes it a versatile tool for optimization in various fields.
Robustness:To handle noisy and stochastic objective functions to makes it as an effective optimization algorithm for real-world problems where objective functions are often noisy and difficult to model accurately.
Simple Implementation:This algorithm is relatively easy to implement and requires minimal tuning of algorithm parameters. This makes it an attractive option for researchers and practitioners who need a effective and simple optimization algorithm.
Fast Convergence: The AVOA is able to converge to good solutions quickly. This is because the algorithm uses the feeding behavior of African vultures to guide the search process towards promising solutions.
While the AVOA has several advantages, it also consider some potential drawbacks. Some of the main limitations of the AVOA algorithm are listed as:
Limited interpretability:While the AVOA algorithm is inspired by the natural behavior of African vultures, it can be difficult to interpret the behavior of the algorithm and understand why it is finding good solutions. This can make it challenging to diagnose the problems to improve the algorithm in specific ways.
Limited scalability:AVOA algorithm is effective for many optimization problems, may not scale well to very large problems. The group behavior of African vultures may not be sufficient to effectively explore the search space of very large problems, and alternative approaches may be required.
Parameter sensitivity:Although the AVOA algorithm is relatively very easy to implement and requires minimal tuning, it can be sensitive to the selection of algorithm parameters. In some areas, a careful parameter tuning may be needed to achieve better results.
Lack of theoretical guarantees:This AVOA algorithm has been shown to be effective in practice, there are currently limited theoretical analyses that provide guarantees about the algorithm-s performance. This can make it difficult to predict how the algorithm will perform in various settings.
The AVOA has several practical applications in various fields. Some examples of practical applications of AVOA are detailed as:
Financial modeling:The AVOA algorithm has been applied in financial modeling problems, such as risk management, and portfolio optimization. It is used to optimize investment strategies, leading to improved returns and reduced risk.
Image and signal processing:Applied in image and signal processing problems, such as feature extraction, image segmentation, and signal denoising. It also used to optimize the parameters of machine learning algorithms, and leading to improved the performance and accuracy.
Renewable energy:Used to optimize the design and control of renewable energy systems, such as solar panels, and wind turbines. AVOA can be used to optimize the placement and configuration of components, leading to improve the efficiency and reduced cost.
Transportation planning:AVOA algorithm has been used to optimize transportation planning problems, such as vehicle routing and scheduling.It can be used to optimize routes and schedules of vehicles, leading to reduced travel cost and time.
Engineering design:The AVOA algorithm has been used to optimize the design of mechanical components, such as bearings, structural components, and gearboxes. AVOA can efficiently search for optimal designs, leading to improved performance and used to reduced weight and its cost.
Lack of extensive research:This is still a relatively new optimization algorithm, with limited research on its effectiveness and limitations compared to other algorithms.
Limited applicability:Mainly suited for continuous optimization problems, and its applicability to other types of optimization problems, such as discrete optimization problems, is still limited.
Complexity:The AVO algorithm has several complex equations and parameters that need to be set, which can make its implementation and interpretation challenging for users.
Lack of standardization:The several parameters that need to be set, including the population size, number of iterations, and mutation rate, among others. However, there is no standardization on how these parameters should be set, and this can affect the algorithm performance.
Sensitivity to initial parameters:It can be sensitive to the initial parameter values, which can affect its convergence rate and the quality of solutions found.
A comparative study of AVOA and other meta-heuristic algorithms:Conduct a comparative study to evaluate the performance of AVOA in solving optimization problems in comparison with other popular meta-heuristic algorithms, such as Particle Swarm Optimization (PSO), Genetic Algorithms (GA), Ant Colony Optimization (ACO).
AVO algorithm is still has a much more potential for its development and future directions. Here are some possible future directions for the AVO algorithm:
