Behavior of Artificial Feeding Birds (AFB) using Meta-heuristics method

1. Introduction

    Artificial Feeding Birds (AFB), a new metaheuristic inspired by the very trivial behavior of birds searching for food. AFB is very simple, yet efficient, and can be easily adapted to various optimization problems. Many algorithms have been inspired by nature [1]. More recently, researchers inspired themselves from the behavior of animals for inventing new optimization algorithms: social organization of insects like ants and honey bees, cohesion within a swarm in flight, communication by light between fireflies, parasitic behavior of cuckoo, ability of pigeons to orientate themselves spatially according to the sun position and the North Pole direction. These algorithms typically rely on swarm intelligence, i.e. they consider a population of agents that interact between themselves and with their environment. These agents are very simple but they can achieve complex tasks together, and in particular they can solve optimization problems. Most of the meta-heuristics were actually inspired by exceptional or extraordinary animal behaviors. For instance, the ability of fireflies to emit light is exceptional: only a few animal species are able to emit light. Even the social organizations of insects are quite rare: many species do not organize themselves in huge colonies (even not all bee species). However, from an evolutionary point of view, the most efficient behaviors lead to a higher chance of survival and thus, they are expected to be observed more frequently [2]. Consequently, we might regard exceptional behaviors as poorly efficient ones (in terms of performances or capability of adaptation), and common behaviors as more efficient. Artificial Feeding Birds (AFB), a new metaheuristic that follows a different kind of inspiration: it has been inspired by a very trivial and common behavior that we observed on birds like pigeons, when they are searching for food on sidewalks or in a garden. Our hypothesis is that, if pigeons are so common, it is because their food search strategy is efficient, and thus it is an interesting inspiration source for algorithms.

     It describes the behavior that we observed on pigeons and other birds searching for food. It presents the metaheuristic algorithm, and its adaptation to two problems: unconstrained global nonlinear optimization and ordering optimization. It presents various experiments performed for determining parameters values, and for testing AFB on several benchmark functions, on TSP and on rainbow boxes optimization, and comparing AFB with other metaheuristics. The birds on the periphery of a group have more chance of being attacked by the predators than those in the centre [3]. Studies suggested that animals foraging in the centre of flock may move to their neighbors to protect themselves from being attacked by the predators. Each bird would try to move towards the centre of the flock as they perceive it. Birds obviously would fly to another site for foraging or just for escaping from predators. After they arrive at a new site, they would search for food again. It is usually observed that there exist producers and scroungers in flock-feeding birds.

2. Life Cycle of Artificial Feeding Birds (Pigeon)

Fig1: Lifecycle of Pigeon

Pigeons will breed between 4 and 8 times a year depending on resource availability (food, water) and climate. In warmer conditions, often found in inner city areas, they can breed all year round [4]. They usually lay two eggs a few days apart. Even once this hatch a new batch of eggs may be laid before the previous young have fledged and left the nest. This habit of regular breeding explains why, when conditions are ideal, large colonies and flocks can form in a very short time, even with eggs falling from nests and chicks dying. This also explains, why after a cull or population reduction operation [5]. Such as trapping or shooting, colonies have a huge capacity for recovery. Not only can these actions deliver a short term effect, any direct approach to control birds by lethal means, must respect and comply with various legal matters not least of which those are relating to animal welfare.

3. Artificial Feeding Birds (AFB)

    Our observations were carried initially on pigeons, and then in groups mixing various types of birds feeding at the same place [6]. Pigeons are very common birds in European towns and they are easy to observe. They feed by pecking seeds or crumbs of food on the ground. When no food is in reach, they explore their environment, using the two mode of movement at their disposal: walking and flying. Pigeon performs four types of move when searching for food.

  • Walking to a new position close to his current position
  • Flying and landing at an arbitrary semi-random position
  • Flying and returning to a memorized position rich in food
  • Flying and landing close to another pigeon

      Typically, a pigeon walks for searching food (one or several move 1). After a while, if no food is found, he flies and go to a random place (move 2), to a memorized position (move 3) or join another pigeon (move 4). Then, he begins to walk again (move 1), etc. This simple behavior optimizes the food search. Move 1 (walk) allows a local search. This is meaningful because there is a high probability to find food close to a position where food has already been found (e.g. if crumbs of a sandwich are present somewhere, it is probable to find other crumbs of the same sandwich nearby) [7]. Move 2 (fly to random position) allows the random exploration of space. Move 3 (return to a memorized position) allows retrieving food, or continuing to look for it in the surroundings. Move 4 (join another bird) allows benefit from the food that the other bird might have found. This leads to big groups of pigeons when an important quantity of food is available in a given place. These observations were carried on pigeons; however, many other birds present a similar behavior, including sparrows and gooses. When several species of birds are mixed together and feed at the same place, we observed that the size of the bird has an impact on move 4 (join another bird): a big bird can join a smaller one. On the contrary, a small bird is frightened by bigger ones and do not join them. We observed this behavior in population mixing gooses and pigeons [8].

     A metaheuristic inspired by the bird feeding behavior. We consider a multi-agent system, each agent being an artificial bird. The position of each bird corresponds to a candidate solution for the optimization problem. Each bird also keeps in memory the best position he found, i.e. the one corresponding to the solution that minimizes the best the cost function. When the current position of a bird is better than the memorized position, the current position is memorized and the bird is considered to “have fed”. The metaheuristic performs several cycles. In each cycle, each bird performs one of the four moves described previously. For a given bird, the next move is determined as follows: if the bird has flown in the previous cycle, he walks. If the bird has eaten in the previous cycle, he walks [9]. Otherwise, one of the four moves is randomly chosen, with different probabilities associated with each move. In addition, we considered two sizes of birds: small and big ones. Only big birds can perform move 4 and join another (small or big) bird. While this rule does not exactly match our observation, it efficiently avoids that all birds get stuck in a local minimum [10].

Fig2: Artificial Feeding Birds (AFB)

3.1. Steps for Artificial Feeding Birds (AFB)  

  • Location
  • Select a few quality bird feeders
  • Fill those feeders with quality bird food
  • Install a sturdy bird feeder pole
  • Provide water
  • Checklist of feeding station supplies & accessories

3.1.1. Location

     Finding a fantastic location in your backyard is critical to having a successful bird feeding station. You can have the best bird feeders, food, or pole in the world, but it won’t matter much if the birds are hesitant to visit [11]. Birds like to feel safe and have a quick getaway in case of predators. Many birds (such as cardinals) will hang out in shrubs or trees around your feeders until they feel comfortable enough to come out and eat [12].

3.1.2. Select a few quality bird feeders

     Regardless, hanging a few feeders is the foundation when setting up your bird feeding station (hence the name). Almost everything else on this list is negotiable, but you have to provide a source of food to see birds consistently [13]. To attract an array of different species, here is my recommendation for the five different types of bird feeders you should use in your backyard.

3.1.3. Fill those feeders with quality bird food

     Bird food is not created equal. This statement is so important it’s worth repeating. It’s human nature to buy the cheapest item in a store, and bird food is no different [14]. There are lots of bird seed mixes on the market that are incredibly inexpensive. The problem is that the birds you are trying to attract won’t eat half of the junk in most of them [15].

3.1.4. Install a sturdy bird feeder pole

      I’m a big fan of having a sturdy and tough bird feeder pole. It’s going to cost a bit more to acquire up front than a cheap shepherds hook, but I think the cost is justified.

3.1.5. Provide water

    When you hang a feeder in your backyard, you are only going to attract the species of birds that eat that specific food. Only certain birds are considered “feeder birds.” Many species will never visit your feeding station regardless of the food you offer [16].

3.1.6. Checklist of feeding station supplies & accessories

     Over time I have accumulated specific supplies that have helped with the maintenance and upkeep of my bird feeding station [17]. Please excuse the randomness, but here is a list of some of the things I found as I went through my shed, garage, and home that help in some way.

3.2. Flow Chart of Artificial Feeding Birds (AFB)

Fig3: Flowchart of AFB

3.3. Example

Algorithm: fly ( ) and walk ( ) functions for optimization problems in Rd [18].

4. Numerical Method of Artificial Feeding Birds (AFB) 

    Numerical method of Artificial Feeding Birds (AFB) in given [19],

5. Applications of AFB

  • Neural network
  • Travelling salesman problem [20]
  • Visual sensitivity
  • Habitat selection [21]
  • Light pollution

Fig4: Applications of AFB

6. Applications of AFB

  • The metaheuristic is very simple.
  • It provides good results [22].
  • It is easy to adapt to new optimization problems, and in particular it makes no assumption on the solutions space and does not require the computation of distances between solutions.
  • Reduced foraging efficiency in low light intensities could be caused by difficulties in detecting and handling prey items [23].
  • Maintenance of the habitat mosaic is a need for a conservation- based management of the dredge islands [24].
  • Artificial light at night may not be as important for driving the timing of foraging behavior in winter as previously thought, but it remains to be seen whether this represents a missed opportunity to extend the foraging period or an adaptive response [25].

Reference

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 [8] Bengtsson, H. and Rydn, O. (1983). Parental feeding rate in relation to begging behavior in asynchronously hatched broods of the great tit Parus major. Behavioral Ecology and Sociobiology, 12(3), pp.243-251.

[9] Rutz, C., Burns, Z., James, R., Ismar, S., Burt, J., Otis, B., Bowen, J. and St Clair, J. (2012). Automated mapping of social networks in wild birds. Current Biology, 22(17), pp.R669-R671.

[10] Scarton, F. and Montanari, M. (2015). Use of artificial intertidal sites by birds in a Mediterranean lagoon and their importance for wintering and migrating waders. Journal of Coastal Conservation, 19(3), pp.321-334.

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[13] Clewley, G., Plummer, K., Robinson, R., Simm, C. and Toms, M. (2015). The effect of artificial lighting on the arrival time of birds using garden feeding stations in winter: A missed opportunity?. Urban Ecosystems, 19(2), pp.535-546.

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[15] Lindegarth, M. and Chapman, M. (2001). Testing hypotheses about management to enhance habitat for feeding birds in a freshwater wetland. Journal of Environmental Management, 62(4), pp.375-388.

[16] Santos, C., Miranda, A., Granadeiro, J., Lourenço, P., Saraiva, S. and Palmeirim, J. (2010). Effects of artificial illumination on the nocturnal foraging of waders. Acta Oecologica, 36(2), pp.166-172.

[17] Sánchez-Zapata, J., Anadón, J., Carrete, M., Giménez, A., Navarro, J., Villacorta, C. and Botella, F. (2005). Breeding waterbirds in relation to artificial pond attributes: implications for the design of irrigation facilities. Biodiversity and Conservation, 14(7), pp.1627-1639.

[18] Orams, M. (2002). Feeding wildlife as a tourism attraction: a review of issues and impacts. Tourism Management, 23(3), pp.281-293.

[19] Fretwell, S. and Lucas, H. (1969). On territorial behavior and other factors influencing habitat distribution in birds. Acta Biotheoretica, 19(1), pp.16-36.

[20] Muiruri, E., Rainio, K. and Koricheva, J. (2015). Do birds see the forest for the trees? Scale-dependent effects of tree diversity on avian predation of artificial larvae. Oecologia, 180(3), pp.619-630.

[21] Kingsolver, J. and Daniel, T. (1983). Mechanical determinants of nectar feeding strategy in hummingbirds: energetics, tongue morphology, and licking behavior. Oecologia, 60(2), pp.214-226.

[22] Murray, M., Becker, D., Hall, R. and Hernandez, S. (2016). Wildlife health and supplemental feeding: A review and management recommendations. Biological Conservation, 204, pp.163-174.

[23] Weale, M., Whitwell, D., Raison, H., Raymond, D. and Allen, J. (2000). The influence of density on frequency-dependent food selection: a comparison of four experiments with wild birds. Oecologia, 124(3), pp.391-395.

[24] Köhler, A., Raubenheimer, D. and Nicolson, S. (2012). Regulation of nutrient intake in nectar-feeding birds: insights from the geometric framework. Journal of Comparative Physiology B, 182(5), pp.603-611.

[25] Sulaiman, M. and Salhi, A. (2015). A Seed-Based Plant Propagation Algorithm: The Feeding Station Model. The Scientific World Journal, 2015, pp.1-16.

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