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Artificiel Bee Colony (ABC) Algorithme Isfahan University of Technology Fall 2010 1 Elham Seifossadat Faegheh Javadi.

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Presentation on theme: "Artificiel Bee Colony (ABC) Algorithme Isfahan University of Technology Fall 2010 1 Elham Seifossadat Faegheh Javadi."— Presentation transcript:

1 Artificiel Bee Colony (ABC) Algorithme Isfahan University of Technology Fall Elham Seifossadat Faegheh Javadi

2 A BEE COLONY OPTIMIZATION ALGORITHM TO JOB SHOP SCHEDULING Job shop scheduling problems are considered to be a member of a large class of intractable numerical problems known as NP-hard. Job shop scheduling is concerned with finding a sequential allocation of competing resources that optimizes a particular objective function. Isfahan University of Technology Fall

3 3 A BEE COLONY OPTIMIZATION ALGORITHM TO JOB SHOP SCHEDULING Isfahan University of Technology Fall A finite set J of n jobs to be processed on a finite set M of m machines. Each job Ji must be processed on every machine and consists of a chain of mi operations Oi1, Oi2,…,Oim which have to be scheduled in a pre-determined given order. Oij is the jth operation of job Ji which has to be processed on a machine Mx for a processing time period of τij without interruption and preemption.

4 Isfahan University of Technology Fall A BEE COLONY OPTIMIZATION ALGORITHM TO JOB SHOP SCHEDULING Each machine can process only one job and each job can be processed by only one machine at a time. The longest duration in which all operations of all jobs are completed is referred to as the makespan Cmax.

5 Isfahan University of Technology Fall A BEE COLONY OPTIMIZATION ALGORITHM TO JOB SHOP SCHEDULING Ai be the set of ordered pairs of operations constrained by the precedence relations for each job Ji. For each machine Mx, the set Ex describes the set of all pairs of operations to be performed on the machine. For each operation Oij, let its earliest possible process start time be Tij.

6 Isfahan University of Technology Fall A BEE COLONY OPTIMIZATION ALGORITHM TO JOB SHOP SCHEDULING

7 Isfahan University of Technology Fall

8 A BEE COLONY OPTIMIZATION ALGORITHM TO JOB SHOP SCHEDULING Isfahan University of Technology Fall

9 A BEE COLONY OPTIMIZATION ALGORITHM TO JOB SHOP SCHEDULING The challenge is to adapt the self-organization behavior of the colony for solving job shop scheduling problems. There are two major characteristics of the bee colony in searching for food sources: waggle dance and forage (or nectar exploration). Isfahan University of Technology Fall

10 A BEE COLONY OPTIMIZATION ALGORITHM TO JOB SHOP SCHEDULING waggle dance A forager fi on return to the hive from nectar exploration will attempt with probability p to perform waggle dance on the dance floor with duration D = di A, it will also attempt with probability ri to observe and follow a randomly selected dance. Isfahan University of Technology Fall

11 A BEE COLONY OPTIMIZATION ALGORITHM TO JOB SHOP SCHEDULING Isfahan University of Technology Fall Profitability rating for a forager:

12 A BEE COLONY OPTIMIZATION ALGORITHM TO JOB SHOP SCHEDULING Isfahan University of Technology Fall The bee colonys average profitability rating:

13 A BEE COLONY OPTIMIZATION ALGORITHM TO JOB SHOP SCHEDULING Isfahan University of Technology Fall The dance duration:

14 A BEE COLONY OPTIMIZATION ALGORITHM TO JOB SHOP SCHEDULING Isfahan University of Technology Fall Adjusting Probability of Following a Waggle Dance:

15 A BEE COLONY OPTIMIZATION ALGORITHM TO JOB SHOP SCHEDULING Forage (Nectar Exploration) Isfahan University of Technology Fall A population of L foragers is defined in the colony. When a forager is at a specific node, it can only move to next node that is defined in a list of presently allowed nodes, imposed by precedence constraints of operations.

16 A BEE COLONY OPTIMIZATION ALGORITHM TO JOB SHOP SCHEDULING Isfahan University of Technology Fall A forager chooses the next node from the list according to the state transition rule:

17 A BEE COLONY OPTIMIZATION ALGORITHM TO JOB SHOP SCHEDULING Isfahan University of Technology Fall The rating ρij of the edge (directed) between node I and node j is given by:

18 A BEE COLONY OPTIMIZATION ALGORITHM TO JOB SHOP SCHEDULING Isfahan University of Technology Fall

19 A BEE COLONY OPTIMIZATION ALGORITHM TO JOB SHOP SCHEDULING The performance of the honey bee colony scheduling approach is studied by evaluating them on the following 82 job shop problem instances. The sizes of these problems range from 6 to 50 jobs and 5 to 20 machines. Isfahan University of Technology Fall

20 A BEE COLONY OPTIMIZATION ALGORITHM TO JOB SHOP SCHEDULING Isfahan University of Technology Fall

21 A BEE COLONY OPTIMIZATION ALGORITHM TO JOB SHOP SCHEDULING Isfahan University of Technology Fall

22 Bee Colony Optimization (BCO) Isfahan University of Technology Fall

23 Bee Colony Optimization (BCO) There are two alternating phases (forward pass and backward pass) constituting single step in the BCO algorithm. The hive is an non-natural object, with no precise location and does not influence the algorithm execution. Isfahan University of Technology Fall

24 Bee Colony Optimization (BCO) In each forward pass, every artificial bee visits NC solution components, creates partial solution, and after that returns to the hive. Isfahan University of Technology Fall

25 Bee Colony Optimization (BCO) Isfahan University of Technology Fall

26 Bee Colony Optimization (BCO) In the backward pass, all artificial bees share information about the quality of their partial solutions. Having all solutions evaluated, each bee decides with a certain probability whether it will stay loyal to its solution or not. Isfahan University of Technology Fall

27 Bee Colony Optimization (BCO) Isfahan University of Technology Fall

28 Bee Colony Optimization (BCO) Isfahan University of Technology Fall

29 Bee Colony Optimization (BCO) When all solutions are completed the best one is determined, it is used to update global best solution and an iteration of the BCO is accomplished. At this point all B solutions are deleted, and the new iteration could start. The BCO runs iteration by iteration until a stopping condition is met. Isfahan University of Technology Fall

30 Bee Colony Optimization (BCO) B - The number of bees in the hive; NC - The number of constructive moves during one forward pass. Isfahan University of Technology Fall

31 Bee Colony Optimization (BCO) (1) Initialization: an empty solution is assigned to each bee; (2) For each bee: // (the forward pass) (a) Set k = 1; // (count constructive moves in the forward pass) (b) Evaluate all possible constructive moves; (c) Choose one move using the roulette wheel; (d) k = k + 1; If k NC Goto step (b). (3) All bees are back to the hive; // (backward pass starts) (4) Evaluate (partial) objective function value for each bee; (5) Each bee decides randomly whether to continue its own exploration and become a recruiter, or to become a follower; (6) For each follower, choose a new solution from recruiters by the roulette wheel; (7) If solutions are not completed Goto step 2; (8) Evaluate all solution and find the best one; (9) If the stopping criteria is not met Goto step 2; (10) Output the best solution found. Isfahan University of Technology Fall

32 Bee Colony Optimization (BCO) Loyalty decision Isfahan University of Technology Fall

33 Bee Colony Optimization (BCO) Isfahan University of Technology Fall Recruiting process

34 Scheduling Independent Tasks by BCO Let T = {1, 2,..., n} be a given set of independent tasks, and P = {1, 2,...,m} a set of identical machines. The processing time of task i (i = 1, 2,..., n) is denoted by li. Isfahan University of Technology Fall

35 Scheduling Independent Tasks by BCO Isfahan University of Technology Fall Probability pi that specific bee chooses task i was equal:

36 Scheduling Independent Tasks by BCO Isfahan University of Technology Fall Probability pj that specific bee chooses processor j was calculated as:

37 Scheduling Independent Tasks by BCO Isfahan University of Technology Fall

38 References A BEE COLONY OPTIMIZATION ALGORITHM TO JOB SHOP SCHEDULING- Chong, Low, Sivakumar, and Gay-Proceedings of the 2006 Winter Simulation Conference. Bee Colony Optimization: The Applications Survey - DUˇSAN TEODOROVI´C TATJANA DAVIDOVI´C and MILICA ˇSELMI´C- ACM Transactions on Computational Logic,2011. Isfahan University of Technology Fall

39 Thanks for your attention!


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