Author: Huang Jingzhi | University of Chinese Academy of Sciences
Training unit: Institute of Physics, Chinese Academy of Sciences
Review: Wang Ting | Associate Professor, Institute of Physics, Chinese Academy of Sciences
Fennec fox
The fennec fox, whose scientific name is Fennec fox, is a small, nocturnal canine that lives mainly in desert areas of northern Africa and western Asia. This fox gets its name from its conspicuously large ears, which are not only the largest of all fox species in terms of proportion, but also very large relative to its body size. The fennec fox is smaller, usually no more than 60 cm in length, with a tail of about 30 cm long, ears that account for more than half the length of the head, and a weight of about 1.5 to 3.5 kg. Their fur is usually sandy or yellowish, a color that blends in with the desert environment in which they live, helping with camouflage and concealment. In terms of food, fennec foxes are omnivores that feed mainly on plants, fruits, insects, small mammals, and birds. Their eating habits also change with the seasons and the availability of food.
So what's the use of such big ears?
The large ears of the fennec fox have a variety of functions. First, they help the fennec fox dissipate heat, and due to the network of blood vessels inside the ears, the heat is dissipated as blood flows through these ears, thus helping the animal maintain its body temperature in the hot desert environment. Secondly, these ears are also excellent hearing tools, helping fennec foxes to catch faint sounds from several kilometers away, which is essential for predation and avoiding predators.
Fennec foxes are the smallest species of foxes, and they have some unique survival skills, including excellent digging abilities and strategies to evade predators.
The fennec fox's digging ability is very strong, mainly due to their keen hearing and strong forelimbs. The large ears of the fennec fox provide a very keen sense of hearing. They are able to hear the faint sounds of their prey underground, allowing them to pinpoint the location of their prey. The forelimbs of fennec foxes are well adapted to digging work, and their claws and forefoot structure allow them to dig efficiently in sand. Once prey is spotted, fennec foxes quickly begin digging, their paws able to move quickly through sand until they catch their prey. This digging ability not only helps fennec foxes obtain food, but is also a key adaptive trait for them to survive in desert environments.
The fennec fox will show great evasion skills when confronted with predators. Since fennec foxes are very fast animals, they can reach very high speeds in a short period of time, which allows them to quickly escape from predators. In addition, fennec foxes can also suddenly change the direction of their running when they escape, and this irregular movement pattern makes it difficult for predators to predict their whereabouts, thus increasing the chances of escape. Even smarter, fennec foxes also use desert terrain to hide from predators, for example, they may use sand dunes or vegetation to block their view and escape tracking.
Fennec fox search optimization algorithm
The escape strategies we have just introduced allow the fennec fox to effectively avoid predators in the wild and maintain the continuation of the population. These behaviors not only demonstrate the adaptability of the fennec fox to the environment, but also provide inspiration for scientists and engineers, especially in the field of computer search optimization algorithms, the biological behavior of the fennec fox has contributed to the development of biomimicry and optimization algorithms.
For example, there is a fennec fox optimization (FFA) algorithm named after the fennec fox in the search algorithm, which is inspired by the mining ability and escape strategy of the fennec fox we just mentioned [1]. The two major problems of the search algorithm are how to improve the efficiency of local search and how to avoid falling into the local optimal solution in global search. The FFA algorithm guides the search process by simulating these two behaviors to find the optimal solution to the problem:
In the local search, the FFA algorithm simulates the behavior of fennec fox mining in the search space, and finds a possible better solution by exploring in the neighborhood of the current solution. This local search helps the algorithm to perform a detailed search near a known solution, thereby improving the quality of the solution.
In terms of global search, the FFA algorithm also simulates the behavior of fennec foxes to evade predators, by randomly jumping in the entire search space to avoid falling into the local optimal solution, and to find the global optimal solution. This global search helps the algorithm maintain diversity and avoid premature convergence.
Through the combination of these two behaviors, the FFA algorithm strikes a balance between exploration (to avoid falling into local optimum) and utilization (to improve the current solution), so that it can search the solution space more efficiently when solving optimization problems, and improve the probability of finding the global optimal solution. This balance is key to the success of meta-heuristics, as it allows the algorithm to be able to explore deeply for the most promising regions while maintaining a broad search.
Other biomimetic optimization algorithms
In addition to the fennec fox algorithm, there are also the following search optimization algorithms that imitate organisms:
The Gray Wolf Optimization Algorithm (GWO) is an optimization method that simulates the social structure and hunting strategy of gray wolves. In this algorithm, the gray wolf population is divided into four levels: alpha, beta, delta, and omega. The alpha wolf is the leader and leads the decision-making, the beta and delta wolves assist the alpha wolf in hunting activities, and the omega wolf is in a subordinate position, usually following the actions of other members. The GWO algorithm iteratively updates the solution by simulating the hunting process of a gray wolf colony, including round-up, pursuit, and attack. In addition, an adaptive parameter is introduced to adjust the proportion of global search and local development to achieve a better solution [2].
The Whale Optimization Algorithm (WOA) is inspired by the hunting behavior of humpback whales, specifically the unique way in which they hunt with bubble nets. In WOA, the candidate solution is treated as a pod of whales, while the optimal solution represents the predation target. The algorithm updates the whale's location through three main mechanisms: encircling prey, bubble net attack, and searching for prey. In the encirclement phase, the whale swims around the prey, in the bubble net attack phase, it gradually approaches the prey in a spiral path, and in the search phase, it involves the act of searching for prey at random. The combination of these three mechanisms enables WOA to be effectively explored in the search space [3].
The Sparrow Search Algorithm (SSA) mimics the behavior patterns of sparrows foraging and alerting predators. In SSA, sparrow groups are divided into two categories: explorers and followers. Explorers seek food around known best locations and utilize the Levy flight mode to enhance global search capabilities, while Followers follow Explorers in search of food in a smaller area. Once the explorer spotted signs of a predator, the entire sparrow colony would quickly fly to safety. This mechanism enables SSA to achieve a balance between global search and local development, and effectively solve various optimization problems [4].
The above search optimization algorithms are modeled on different biological groups for learning, and interested partners can learn about it. It can be seen that nature is a treasure trove of human cognition, and we can still learn a lot from nature despite the rapid development of human technology.
bibliography[1]E. Trojovská, M. Dehghani, and P. Trojovský, "Fennec Fox Optimization: A New Nature-Inspired Optimization Algorithm," IEEE Access 10, 84417–84443 (2022).
[2.]Behavior of Grey Wolf Optimization (GWO) Algorithm using Meta-heuristics method
[3].S. Mirjalili and A. Lewis, "The Whale Optimization Algorithm," Adv. Eng. Softw. 95, 51–67 (2016).
[4]. Sparrow Search Algorithm (SSA): A Swarm Intelligence Optimization Algorithm for the Application to Solve Practical Engineering Examples
Editor: Mu Zi