VOLUME 2, MAY 2009
• Name: ENGR. ZULKIFLI ZAINAL ABIDIN Grad. IEM
• Joining date with URRG: 19 November 2008
• Field of Study: PhD in Robotic: Animal-inspired Swarm Intelligence and Autonomous Surface Vehicles
• Family: Wife; Hasnida Hassan (INTEL Penang), Daughters; Hana Alisha & Ariana Hani.
• Home address: Casa Perdana, Taman Pauh Jaya, Prai.
• D.O.B: 14 June 1979
• Hobby: Gadget Collector
• Sport: Badminton
• Favorite food & drink in Penang: Garlic Cheese Nan, KAPITAN & Pandan Coconut, Lorong Abu Siti, Georgetown.
• B.Eng Computer & Information Engineering, IIUM, 2003
• M.Sc Electrical & Electronics Engineering, USM, 2007 (By Research). Thesis: Development of a Vision System for Ship Hull Inspection.
• Board of Engineer Malaysia (BEM), Registration date: 27 Jun 2007, (52026A)
• Institute of Electrical and Electronics Engineers (IEEE), Registration date: 4 Sept 2007, (90250137). Robotics and Automation Society & Systems, Man, and Cybernetics Society. (IEEE-RAS Malaysia Exco Member)
• The Institution of Engineers Malaysia (IEM),Registration date: 21 Jan 2008 (29631), Field: Electronic.
Area of Interest
• Vision based Multi-agent System.
• Entertainment and edutainment robotics.
• Underwater Robotics Technology.
• Multimedia integrated system
In this section, we will see how the animals interact with nature in the act of searching for food, mate, provisions and their pattern in moving as a group or as an individual.
Ants are social insect. They live together in organized colonies with at least one ‘Queen’ in their nest.
In the Holy Scriptures, some verses pertaining to them are mentioned. For example, in the Holy Quran,
“At length, when they came to a (lowly) valley of ants, one of the ants said: "O ye ants get into your habitations, lest Solomon and his hosts crush you (under foot) without knowing it." (Quran 27:18.)
When foraging, a swarm of ants interact with their environment locally. Although, there is no leader nor is there a centralize command, the ants still can communicate with each other via pheromones in finding their source of foods and paths. Foraging ants travel for distances of up to 200 meters from their nest  and usually find their way back using pheromone trails. With an average speed of 0.5 cm per second (this varies with the species of ant); a moving ant lays some pheromones (in varying quantities) on the ground, thus marking the path by a trail of this substance. While an isolated ant moves essentially at random, an ant encountering a previously laid trail can detect it and decide with high probability to follow it, thus reinforcing the trail with its own pheromone. According to Dorigo et al. , the collective behaviour that emerges is a form of autocatalytic behavior where the more the number of ants following a trail, the more attractive that particular trail becomes to be followed.
a) Ants follow a path between points A and E.
b) An obstacle is interposed; ants can choose to go around it following one of the two different paths with equal probability.
c) On the shorter path more pheromone is laid down.
Bees, like ants, are specialized species of the wasp. A honey bee queen may lay 2000 eggs per day during spring buildup, but she also must lay 1000 to 1500 eggs per day during the foraging season, mostly to replace the daily casualties, most of which are workers dying of old age
It has been stated in the Holy Quran,
Your Lord revealed to the bees: "Build dwellings in the mountains and the trees, and also in the structures which men erect. Then eat from every kind of fruit and travel the paths of your Lord, which have been made easy for you to follow." From inside them comes a drink of varying colors, containing healing for mankind. There is certainly a sign in that for people who reflect. (Quran, 16:68-69) Sura 16, The Bee (Al-Nahl)
Fig. 3 Bees
A well known scientist has made the following observation,
"If the bee disappears from the surface of the earth, man would have no more than four years to live?" Albert Einstein
The foraging process begins in a colony by scout bees being sent to search for promising flower patches. Scout bees move randomly from one patch to another. Having found the patches which are rated above a certain quality threshold, these scout bees would then deposit their nectar or pollen and eventually perform a “waggle dance” when they return to the hive . This dance is essential for colony communication. It is about: the direction to the source, the distance from the hive, and the quality rating [4, 5]. This information helps the colony to send its bees to the flower patches precisely, without using guides or maps. While harvesting from a patch, the bees monitor its food level. This is necessary to decide upon the next waggle dance when they return to the hive . If the patch is still good enough as a food source, then it will be advertised in the waggle dance and more bees will be recruited to the particular source .
Approximately 75% of the bees from a colony forage within one kilometer while the young field bees only fly within the first few hundred meters. The longer foraging time is, the greater would be the nectar availability.
Figure 4: Distance and direction by waggling dance. Waggling straight up on the vertical comb indicates food source which is located at an azimuthal angle of 00 while waggling straight down indicates food source, located at an azimuthal angle of 1800. Figure is taken from .
Primates have a highly developed brain, usually living in groups with their own complex social systems. Their high intelligence allows them to adapt their behavior successfully to different environments. Included in this group are monkeys, apes and humans.
According to A. Mucherino and O. Seref , when the monkey climbs up a tree for the first time, it they can only choose the branches of the tree in a random way, because it they do not have any previous experience climbing on that tree. However, when the monkey climbs up the tree again, it they would try to follow the paths that would lead them to good food, allowing the monkeys to discover a set of connected branches of the tree in which there are good food resources. When the monkey discovers a better solution, they remember it. Later, on their way down, the monkeys mark the corresponding branches, and then use these marks for deciding which branches to climb up again. This marking strategy reflects the monkey’s intentions to focus on a part of a tree where it has already found some good solutions. When the monkey decides to restart climbing up, it encounters some previously visited branches on its way up. It then climbs these branches again. The monkey chooses between one of the two tree branches based on the marks it left before. Naturally, the monkey has greater probability of choosing a branch leading to better solutions, and this probability increases with the quality of the solution the branch leads to.
Fireflies (lightning bugs) use their bioluminescence to attract mates or prey. They live in moist places under debris on the ground, others beneath bark and decaying vegetation.
Firefly Algorithm (FA) was developed by Xin-She Yang  at Cambridge University in 2007. It uses the following three idealized rules: 1) all fireflies are unisex so that a firefly will be attracted to other fireflies regardless of their sex; 2) Attractiveness is proportional to their brightness; thus for any two flashing fireflies, the less brighter will move towards the brighter one. The attractiveness is proportional to the brightness and they both decrease as their distance increases. If there is no brighter firefly than a particular one, it will move randomly; 3) the brightness of a firefly is affected or determined by the landscape of the objective function. For maximization problem, the brightness can simply be proportional to the value of the objective function.
In the Holy Quran, 22, verse 73, it was stated:
Mankind! An example has been made, so listen to it carefully. Those whom you call upon besides God are not even able to create a single fly, even if they were to join together to do it. And if a fly steals something from them, they cannot get it back. How feeble are both the seeker and the sought!
The main idea behind this algorithm is based upon Drosophila’s biological behavior; 1) The fly hunts for food and a mate within a one to two month lifespan , 2) The fly flies with Lévy flight motion [11, 12, 13, 14]. 3) It smells the potential location (attractiveness), 4) Then tastes, if not good (fitness / profitability), rejects and goes to another location. To the fly, attractiveness is not necessarily profitable [21, 22, 10, 15]. 5) While foraging or mating, the fly also sends and receives a message with its friends about it foods and mates [16, 17, 18, 19, and 20].
The main steps of the algorithm are given in flowchart Fig. 8. When a fly decides to go for hunting, It will fly randomly (with Lévy flight motion) to find the location guided by a particular odor. While searching, the fly also sends and receives information from its neighbors and makes comparison about the best current location and fitness. If a fly has found its spot, it will then identify the fitness by taste. If the location no longer exists or the taste is ‘bitter’, the fly will go off searching again. The fly will stay around at the most profitable area, sending, receiving and comparing information at the same time. The total number of flies depends upon the number of sources. However, since most of the flies are near to the food source location, then the next generation of flies is considered to be already closeby to the potential food location.
In order to develop a completely new animal inspired algorithm, we have to observe study and learn from the creature’s nature behavior. Each being has its own unique behavior and each provide almost unlimited ways for problem solving. If we can study carefully, we are surely inspired to develop more powerful and efficient new algorithms.
As we can see, all the algorithms are based on the behavior of the animals with slight and small modifications to suit the needs of the algorithm itself. Due to the active nature of research on the particular animals which are still currently being done in laboratories, each significant behavior should be added into the algorithms instead of focusing on the process of hybridization. Although hybrid method is very popular among the researchers, we also rely upon the actual biological criteria of the animal itself. Take for example, the case of hybridization of BA with PSO; BA as we know can only send information about the nectar location on the dance floor. By adding PSO, the message will be sent out of the hive. To us, this is not consistent with the natural behavior of the bee.
Naturally, ants and bees hunt for their colony and serve the queen. Monkeys, fireflies and flies look for food and a mate for themselves. The communication medium for the ant colony is via the pheromone (which is passed from ant to ant while foraging). Bees exercise the ‘waggle dance’ in hives after foraging. There is no group searching for monkeys while climbing for food. A firefly does not share its information while at the same is engaged in finding the best mate. A fly makes contact with its neighbors via neuronal signaling (while searching) and pheromone (while mating). We can observe clearly, one of the main advantages of the fly is that, information sharing
among the group is faster than any of the other animals. Thus, the searching period for optimization for the fly will be shorter.
Theoretically, the whole ant colony loses its direction and energy when being attacked while foraging or when the food is suddenly removed. For bees, only the particular bee concerned will be affected. However if the attacker is close to the hive or swarm of bees, the whole bee colony may ‘fight brutally’. Meanwhile, a fly on the other hand, will still be flying around the potential area hunting for food. It may well be observed too that getting rid of flies while on a picnic or having a barbecue, is not actually an easy task!
Various applications have been carried out recently in the last five years. These include the combinatorial optimization, job scheduling, web-hosting allocation, engineering design optimization, function optimization, reservoir modeling and the TSP, training neural networks, forming manufacturing cells, scheduling jobs for a production machine, finding multiple feasible solutions to a preliminary design problems, data clustering, optimizing the design of mechanical components, multi-objective optimization., tuning a fuzzy logic controller and many others. It would be futile to mention all of them.
The extraordinary thing about the entire animal inspired metaheuristic algorithms is that, they all share one thing in common; in a short period of time, animals try to optimize their searching space while hunting for food and mates. As humans, we are no different i.e. we also deal with optimization our daily life, such as budgeting our expenditure, traveling from one place to another, or even looking for the perfect ‘soulmate’. However, the only difference is the way in which we carry out our deals, as compared to the creatures. The existence of too ‘many neurons’ or disturbances make our decision become more complex, even though the solution might just be right in front of our eyes! Man always indulge in the quest for perfection, which may be prove to be quite a problem, as our lifespan is not very long, some might say. However, each animal algorithm has its own list of strengths and weaknesses due to its own ‘natural’ ability.
 Fred GLover (1986), “Future Paths for Integer Programming and Links to Artificial Intelligence,” Computer. & Ops. Res. Vol. 13, No.5, pp. 533-549.
 Carrol CR, Janzen DH (1973), “Ecology of foraging by ants,” Annual Review of Ecology and Systematics 4: 231–257. doi:10.1146/annurev.es.04.110173.001311.
 M. Dorigo, V. Maniezzo & A. Colorni (1996), “Ant System: Optimization by a Colony of Cooperating Agents,” IEEE Transactions on Systems, Man, and Cybernetics–Part B, 26 (1): 29–41.
 J. Deneubourg, S. Aron, S. Goss, and J. Pasteels (1990), “The self-organizing exploratory pattern of the argentine ant,” Journal of Insect Behaviour, 3:159–168.
 G. Iba (1989), “A heuristic approach to the discovery of macro-operators,” Machine Learning, 3:285–317.
 Nyree Lemmens, Steven de Jong, Karl Tuyls, and Ann Nowe (2007), “A Bee Algorithm for Multi-Agent Systems: Recruitment and Navigation Combined,” In Proceedings of ALAG, an AAMAS workshop.
 F. Barth. Insects and flowers (1982), “The biology of a partnership,” Princeton University Press, Princeton, New Jersey.
 A. Mucherino and O. Seref, (2007), “Monkey Search: A Novel Meta-Heuristic Search for Global Optimization,” AIP Conference Proceedings 953, Data Mining, System Analysis and Optimization in Biomedicine, 162–173.
 Yang X. S. (2008), “Nature-inspired Metaheuristic Algorithms,” Luniver Press.
 Andy M. Reynolds, Mark A. Frye (2007), “Free-Flight Odor Tracking in Drosophila Is Consistent with an Optimal Intermittent Scale-Free Search,” Evolutionary Biology, PlusONE
 Bartumeus, F., M. G. E. da Luz, G. M. Viswanathan, and J.Catalan. (2005), “Animal search strategies: a quantitative. random-walk analysis,” Ecology 86:3078–3087.
 Viswanathan, G. M., S. V. Buldyrev, S. Havlin, M. G. E. da Luz, E. P. Raposo, and H. E. Stanley (1999), “Optimizing the success of random searches,” Nature 401:911–914.
 Maye, A.; Hsieh, C.; Sugihara, G. and Brembs, B. (2007), “Order in spontaneous behavior,” PLoS One, May 16.DOI number: 10.1371/journal.pone.0000443
 University of California - Los Angeles (2008, February 19), “Fruit Flies Show Surprising Sophistication In Locating Food,” Source.,ScienceDaily. Retrieved December 22, 2008,
 Ilya Nemenman, Geoffrey D. Lewen, William Bialek, Rob R. de Ruyter van Steveninck, (2008), “Neural Coding of Natural Stimuli: Information at Sub-Millisecond Resolution,” PLoS Comput Biol 4(3): e1000025. doi:10.1371/journal.pcbi.1000025.PLoS Comput Biol. 2008 March; 4(3): e1000025.
 Tinette S, Zhang L, Robichon A.(2004), “Cooperation between Drosophila flies in searching behavior,” Genes Brain Behav. 2004 Feb;3(1):39-50.
 Joshua J. Krupp, Clement Kent, Jean-Christophe Billeter, Reza Azanchi, Anthony K.-C. So, Julia A. Schonfeld, Benjamin P. Smith, Christophe Lucas, and Joel D. Levine. (2008), “Social Experience Modifies Pheromone Expression and Mating Behavior in Male Drosophila melanogaster,” Current Biology, 2008; DOI: 10.1016/j.cub.2008.07.089
 Clement Kent, Reza Azanchi, Ben Smith, Amanda Formosa, and Joel D. Levine. (2008), “Social Context Influences Chemical Communication in D. melanogaster Males,” Current Biology, 2008; DOI: 10.1016/j.cub.2008.07.088
 Cell Press (2008, September 12), “Flies, Too, Feel The Influence Of Their Peers, Studies Find,” ScienceDaily. Retrieved December 13, 2008, from http://www.sciencedaily.com¬ /releases/2008/09/080911122527.htm
 Gerard Manning (2008), “The WWW Virtual Library: Drosophila,”http://ceolas.org/VL/fly/index.html
 Frye, M.A., M. Tarsitano, and M.H. Dickinson (2003), “Odor localization requires visual feedback during free-flight in Drosophila melanogaster,” Journal of Experimental Biology (featured in Inside JEB by science journalist G.T. Huang) 206: 843-855