INF3490 - Biologically inspired computing Swarm Intelligence, Fuzzy - PowerPoint PPT Presentation

11/5/2018 INF3490 - Biologically inspired computing Swarm Intelligence, Fuzzy Logic Weria Khaksar November, 06, 2018 1

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11/5/2018 Swarm Intelligence: Concept Collective behavior emerged from social insects working under very few rules. 06.11.2018 3 3

11/5/2018 Swarm Intelligence: Concept Fish, birds, ants, termites, lions, … 06.11.2018 4 4

11/5/2018 Swarm Intelligence: Key Features  Simple local rules  Local interaction  Decentralized control  Complex global behavior • Difficult to predict from observing the local rules • Emergent behavior 06.11.2018 5 5

11/5/2018 Swarm Intelligence: General Principles Proximity principle The basic units of a swarm should be capable of simple computation related to its surrounding environment. 06.11.2018 6 6

11/5/2018 Swarm Intelligence: General Principles Proximity principle The basic units of a swarm should be capable of simple computation related to its surrounding environment. Quality principle Apart from basic computation ability, a swarm should be able to response to quality factors, such as food and safety. 06.11.2018 7 7

11/5/2018 Swarm Intelligence: General Principles Proximity principle The basic units of a swarm should be capable of simple computation related to its surrounding environment. 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. 06.11.2018 8 8

11/5/2018 Swarm Intelligence: General Principles Proximity principle The basic units of a swarm should be capable of simple computation related to its surrounding environment. 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 9 9

11/5/2018 Swarm Intelligence: Particle Swarm Optimization (PSO) Mimicking physical quantities such as velocity and position in bird flocking, artificial particles are constructed to “fly” inside the search space of optimization problems. 06.11.2018 10 10

11/5/2018 Swarm Intelligence: Particle Swarm Optimization (PSO)  Initially, a population of particles is distributed uniformly in the search space of the objective function of the optimization problem.  Two quantities are associated with particles, a position vector 𝑦 � and a velocity 𝑤 � . 06.11.2018 11 11

11/5/2018 Swarm Intelligence: Particle Swarm Optimization (PSO)  At each time step, the velocities of particles will be updated according to the following formula: 06.11.2018 12 12

11/5/2018 Swarm Intelligence: Particle Swarm Optimization (PSO) 06.11.2018 13 13

11/5/2018 Swarm Intelligence: Particle Swarm Optimization (PSO) Example PSO algorithm maximizing f(x, y) = -|x**2 - y| (finding squares). 06.11.2018 14 14

11/5/2018 Swarm Intelligence: Ant Colony Optimization (ACO) The most recognized example of swarm intelligence in real world is the ants. To search for food, ants will start out from their colony and move randomly in all directions. 06.11.2018 15 15

11/5/2018 Swarm Intelligence: Ant Colony Optimization (ACO) Once an ant finds food, it returns to colony and leave a trail of chemical substances called pheromone along the path. Other ants can then detect pheromone and follow the same path. 06.11.2018 16 16

11/5/2018 Swarm Intelligence: 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. 06.11.2018 17 17

11/5/2018 Swarm Intelligence: Ant Colony Optimization (ACO) Ant Colony Optimization on Traveling Salesman Problem 06.11.2018 18 18

11/5/2018 Swarm Intelligence: Bee Colony Optimization (BCO) Just like ants, bees have similar food collecting behaviors. Instead of pheromones, bees colony optimization algorithm relies on the foraging behavior of honey bees. At the first stage, some bees are sent out to look for promising food sources. 06.11.2018 19 19

11/5/2018 Swarm Intelligence: Bee Colony Optimization (BCO) After a good food source is located, bees return back to colony and perform a waggle dance to spread out information about the source. Three pieces of information are included: (1) distance, (2) direction, (3) quality of food source. The better the quality of food source, the more bees will be attracted. Therefore, the best food source emerges. 06.11.2018 20 20

11/5/2018 Swarm Intelligence: Bee Colony Optimization (BCO) 06.11.2018 21 21

11/5/2018 Swarm Intelligence: Bee Colony Optimization (BCO) 06.11.2018 22 22

11/5/2018 Swarm Intelligence: Bee Colony Optimization (BCO) 06.11.2018 23 23

11/5/2018 Swarm Intelligence: Bee Colony Optimization (BCO) 06.11.2018 24 24

11/5/2018 Swarm Intelligence: Bee Colony Optimization (BCO) 06.11.2018 25 25

11/5/2018 Swarm Intelligence: Bee Colony Optimization (BCO) Using the Bee colony Algorithm to solve the Knight's Tour Problem 06.11.2018 26 26

11/5/2018 Swarm Intelligence: Cuckoo Search This algorithm is inspired by the brood parasitism behavior of some species of cuckoo. They will lay their eggs in other bird's nest. If the host bird find out about this, it will either throw away the intruding egg or simply abandon the whole nest and start a new one. However, some species of cuckoo are very good at making their eggs the same as the host's egg, and therefore greatly increase the survival probability of their eggs. 06.11.2018 27 27

11/5/2018 Swarm Intelligence: Cuckoo Search Basic rules of cuckoo search:  Each cuckoo lays one egg at a time and dumps it in a randomly chosen nest.  The best nests with high quality of eggs will be brought to the next generation.  The number of host nests is fixed. A host bird will discover the egg is laid by cuckoo by a probability 𝑄 � ∈ �0,1� . The host bird can get rid of the egg or build a new nest. 06.11.2018 28 28

11/5/2018 Swarm Intelligence: Cuckoo Search 06.11.2018 29 29

11/5/2018 Swarm Intelligence: Cuckoo Search Example: Finding the maximum of 2D Michalewicz' function 06.11.2018 30 30

11/5/2018 Swarm Intelligence: Cuckoo Search From left to right: Initial and final positions of the nests marked using dots. 06.11.2018 31 31

11/5/2018 Swarm Intelligence: 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  ... 06.11.2018 32 32

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 33 33

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11/5/2018 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 35

11/5/2018 Fuzzy Logic: What is fuzzy thinking? 06.11.2018 36 36

11/5/2018 Fuzzy Logic: What is fuzzy thinking? ... 06.11.2018 37 37