INF3490 - Biologically inspired computing Swarm Intelligence, Fuzzy - PDF document

11/5/2018 11/5/2018 INF3490 - Biologically inspired computing Swarm Intelligence, Fuzzy Logic Weria Khaksar November, 06, 2018 06.11.2018 2 1 2 11/5/2018 11/5/2018 Swarm Intelligence: Concept Swarm Intelligence: Concept Collective behavior emerged from social insects Fish, birds, ants, termites, lions, … working under very few rules. 06.11.2018 06.11.2018 3 4 3 4 11/5/2018 11/5/2018 Swarm Intelligence: Key Features Swarm Intelligence: General Principles  Simple local rules Proximity principle The basic units of a swarm should be capable of simple  Local interaction computation related to its surrounding environment.  Decentralized control  Complex global behavior • Difficult to predict from observing the local rules • Emergent behavior 06.11.2018 5 06.11.2018 6 5 6

11/5/2018 11/5/2018 Swarm Intelligence: General Principles Swarm Intelligence: General Principles Proximity principle Proximity principle The basic units of a swarm should be capable of simple The basic units of a swarm should be capable of simple computation related to its surrounding environment. computation related to its surrounding environment. Quality principle Quality principle Apart from basic computation ability, a swarm should be able to Apart from basic computation ability, a swarm should be able to response to quality factors, such as food and safety. response to quality factors, such as food and safety. Principle of diverse response Resources should not be concentrated in narrow region. The distribution should be designed so that each agent will be maximally protected facing environmental fluctuations. 06.11.2018 7 06.11.2018 8 7 8 11/5/2018 11/5/2018 Swarm Intelligence: General Principles Swarm Intelligence: Particle Swarm Optimization (PSO) Proximity principle The basic units of a swarm should be capable of simple Mimicking physical quantities such as velocity and position computation related to its surrounding environment. in bird flocking, artificial particles are constructed to “fly” inside the search space of optimization problems. Quality principle Apart from basic computation ability, a swarm should be able to response to quality factors, such as food and safety. Principle of diverse response Resources should not be concentrated in narrow region. The distribution should be designed so that each agent will be maximally protected facing environmental fluctuations. Principle of stability and adaptability Swarms are expected to adapt environmental fluctuations without rapidly changing modes since mode changing costs energy. 06.11.2018 06.11.2018 9 10 9 10 11/5/2018 11/5/2018 Swarm Intelligence: Swarm Intelligence: Particle Swarm Optimization (PSO) Particle Swarm Optimization (PSO)  Initially, a population of particles is distributed uniformly  At each time step, the velocities of particles will be in the search space of the objective function of the updated according to the following formula: optimization problem.  Two quantities are associated with particles, a position vector 𝑦 � and a velocity 𝑤 � . 06.11.2018 11 06.11.2018 12 11 12

11/5/2018 11/5/2018 Swarm Intelligence: Swarm Intelligence: Particle Swarm Optimization (PSO) Particle Swarm Optimization (PSO) Example PSO algorithm maximizing f(x, y) = -|x**2 - y| (finding squares). 06.11.2018 13 06.11.2018 14 13 14 11/5/2018 11/5/2018 Swarm Intelligence: Swarm Intelligence: Ant Colony Optimization (ACO) Ant Colony Optimization (ACO) The most recognized example of swarm intelligence in real Once an ant finds food, it returns to colony and leave a trail world is the ants. To search for food, ants will start out from of chemical substances called pheromone along the path. their colony and move randomly in all directions. Other ants can then detect pheromone and follow the same path. 06.11.2018 06.11.2018 15 16 15 16 11/5/2018 11/5/2018 Swarm Intelligence: Swarm Intelligence: Ant Colony Optimization (ACO) Ant Colony Optimization (ACO) The interesting point is that how often is the path visit by ants is determined by the concentration of pheromone along the path. Since pheromone will naturally evaporate over time, the length of the path is also a factor. Therefore, under all these considerations, a shorter path will be favored because ants following that path keep adding pheromone which makes the concentration strong enough to against evaporation. As a result, the shortest path from colony to foods emerges. Ant Colony Optimization on Traveling Salesman Problem 06.11.2018 17 06.11.2018 18 17 18

11/5/2018 11/5/2018 Swarm Intelligence: Swarm Intelligence: Bee Colony Optimization (BCO) Bee Colony Optimization (BCO) Just like ants, bees have similar food collecting behaviors. After a good food source is located, bees return back to Instead of pheromones, bees colony optimization algorithm colony and perform a waggle dance to spread out relies on the foraging behavior of honey bees. At the first information about the source. Three pieces of information stage, some bees are sent out to look for promising food are included: (1) distance, (2) direction, (3) quality of food sources. source. The better the quality of food source, the more bees will be attracted. Therefore, the best food source emerges. 06.11.2018 19 06.11.2018 20 19 20 11/5/2018 11/5/2018 Swarm Intelligence: Swarm Intelligence: Bee Colony Optimization (BCO) Bee Colony Optimization (BCO) 06.11.2018 06.11.2018 21 22 21 22 11/5/2018 11/5/2018 Swarm Intelligence: Swarm Intelligence: Bee Colony Optimization (BCO) Bee Colony Optimization (BCO) 06.11.2018 23 06.11.2018 24 23 24

11/5/2018 11/5/2018 Swarm Intelligence: Swarm Intelligence: Bee Colony Optimization (BCO) Bee Colony Optimization (BCO) Using the Bee colony Algorithm to solve the Knight's Tour Problem 06.11.2018 25 06.11.2018 26 25 26 11/5/2018 11/5/2018 Swarm Intelligence: Swarm Intelligence: Cuckoo Search Cuckoo Search This algorithm is inspired by the brood parasitism behavior Basic rules of cuckoo search: of some species of cuckoo. They will lay their eggs in other  Each cuckoo lays one egg at a time and dumps it in a bird's nest. If the host bird find out about this, it will either randomly chosen nest.  The best nests with high quality of eggs will be brought to the throw away the intruding egg or simply abandon the whole next generation. nest and start a new one. However, some species of  The number of host nests is fixed. A host bird will discover the cuckoo are very good at making their eggs the same as the egg is laid by cuckoo by a probability 𝑄 � ∈ �0,1� . The host bird host's egg, and therefore greatly increase the survival can get rid of the egg or build a new nest. probability of their eggs. 06.11.2018 06.11.2018 27 28 27 28 11/5/2018 11/5/2018 Swarm Intelligence: Swarm Intelligence: Cuckoo Search Cuckoo Search Example: Finding the maximum of 2D Michalewicz' function 06.11.2018 29 06.11.2018 30 29 30

11/5/2018 11/5/2018 Swarm Intelligence: Swarm Intelligence: Cuckoo Search Other Algorithms  Artificial Immune Systems  Firefly algorithm  Bacterial Foraging  Dolphin Partner Optimization  The Shuffled Frog Leaping  Dolphin echolocation algorithm  The Cat Swarm  Flower pollination algorithm  Invasive weed optimization  Krill herd  Monkey Search  Wolf search  Water flow-like algorithm  Grey Wolf Optimizer  Biogeography-based optimization  Water cycle algorithm  The Fish School Search  The Social spider optimization  Bat-inspired Algorithm  Forest Optimization algorithm  Lion Optimization  ... From left to right: Initial and final positions of the nests marked using dots. 06.11.2018 31 06.11.2018 32 31 32 11/5/2018 11/5/2018 References: 1. Keerthi, S., Ashwini, K., & Vijaykumar, M. V. (2015). Survey Paper on Swarm Intelligence. International Journal of Computer Applications , 115 (5). 2. Keerthi, S., Ashwini, K., & Vijaykumar, M. V. (2015). Survey Paper on Swarm Intelligence. International Journal of Computer Applications , 115 (5). 3. Cui, X. Swarm Intelligence. 4. Hu, Y. Swarm intelligence. 5. Bonabeau, E., & Meyer, C. (2001). Swarm intelligence: A whole new way to think about business. Harvard business review , 79 (5), 106-115. 06.11.2018 06.11.2018 33 34 33 34 11/5/2018 11/5/2018 Fuzzy Logic: What is fuzzy thinking? Fuzzy Logic: What is fuzzy thinking? Fuzzy logic is determined as a set of mathematical principles for knowledge representation based on degrees of membership rather than on crisp membership of classical binary logic. Range of logical values in Boolean and fuzzy logic: (a) Boolean logic; (b) multivalued logic. 06.11.2018 35 06.11.2018 36 35 36