1. ENGG*6140 Optimization for Engineering
Paper review of ENGG*6140
Cooperative Search Techniques
Shaw & Vincent
2019/4/24
ENGG*6140 Optimization for Engineering

2. ENGG*6140 Optimization for Engineering
Outline
Introduction of general Cooperative Search Techniques
Introduction of TECHS approach
Introduction of JSSP (Job-Shop Scheduling problem)
TECHS Approach
Experiments
Related Approach
Conclusion
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ENGG*6140 Optimization for Engineering

3. ENGG*6140 Optimization for Engineering
Introduction of general Cooperative Search Techniques
What is Cooperative Search Technique?
Cooperative search is a parallel strategy for search algorithms where parallelism is obtained by concurrently executing several search programs.
The solution space is implicitly decomposed according to the search strategy of each program.
The programs cooperate by exchanging information on previously explored regions of the solution space.
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ENGG*6140 Optimization for Engineering

4. ENGG*6140 Optimization for Engineering
Introduction of general Cooperative Search Techniques
Why we need Cooperative Search Techniques?
Different search techniques have different strengths and weaknesses.
Genetic Algorithm: good at exploration, but not good for local search
Simulated Annealing: capable of producing near optimal solution, but consumes huge amounts of time.
Tabu search:capable to explore and exploit, but requires lots of parameter tuning
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ENGG*6140 Optimization for Engineering

5. ENGG*6140 Optimization for Engineering
Introduction of TECHS approach
What is TECHS?
TECHS is Teams for Cooperative Heterogeneous Search
(Multi-agent based approach,Genetic Algorithms & Branch-and-Bound method)
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ENGG*6140 Optimization for Engineering

6. ENGG*6140 Optimization for Engineering
Introduction of TECHS approach
What are search agents?
A search agent is a search process with additional communication abilities.
Two components in search process:
Search model: consists of a set of possible search states and a transition relation that defines the possible successor states to each state.
Search control: select for a search state exactly one of the possible successor states defined by the transition relation.
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ENGG*6140 Optimization for Engineering

7. ENGG*6140 Optimization for Engineering
Introduction of TECHS approach
What are possible types of information to exchange?
Positive information: the information that helps finding a solution to a given instance of a search problem.
e.g information intended to direct the search towards the search goal.
Negative information: the information that does not lead towards finding a solution, but characterizes attribute values that are definitely not part of a solution.
e.g information intended to avoid possible steps in the search process.
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ENGG*6140 Optimization for Engineering

8. ENGG*6140 Optimization for Engineering
Introduction of TECHS approach
Why we need referees?
Since each action taken by an agent during its search results in possible information to exchange, it is very important to limit the information that is really exchanged.
Referees: Send-referees & Receive-referees
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ENGG*6140 Optimization for Engineering

9. ENGG*6140 Optimization for Engineering
Introduction of TECHS approach
The usage of Referees:
Send-referee: select information of all types on the side of the sending agent.
Receive-referee: provide an additional filter on the side of the receiving agent.
By adding send- and receive-referees and communication channels between the processes, we get search agents.
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ENGG*6140 Optimization for Engineering

10. ENGG*6140 Optimization for Engineering
Introduction of TECHS approach
Criteria of Send-referees:
Success-driven:
Parts of search states that enable good transitions should be preferred to parts that only enabled bad ones.
Demand-driven:
Rating the information to select w.r.t the control strategy of other agents or w.r.t certain attributes solutions should have or not have.
A further criteria:
very unlike that the other agent will find the information on its own.
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ENGG*6140 Optimization for Engineering

11. ENGG*6140 Optimization for Engineering
Introduction of TECHS approach
About data format:
In order to communicate information of all types, data format are needed to represent information. This format can be used for communication by all systems.
The search systems internally might use totally different data structure.
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12. ENGG*6140 Optimization for Engineering
Introduction of TECHS approach
Criteria of Receive-referees:
Try to measure the impact of the information on the further search of the agent.
This is especially important for positive information, since it tends to broaden the search space if it is not useful.
Filtering negative information is not so critical, but if an agent accumulates too much negative information then the selection process for the next search steps can become time consuming.
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ENGG*6140 Optimization for Engineering

13. ENGG*6140 Optimization for Engineering
Introduction of TECHS approach
Two phases:
Working phase: the agent concentrates on its search.
Cooperation phase: Send-referees select information to be sent away and Receive-referees filters the information received from the other agents.
If all agents use the same time interval for working phases one can organize the information interchange in a synchronized manner, else it has to be asynchronous.
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ENGG*6140 Optimization for Engineering

14. ENGG*6140 Optimization for Engineering
Introduction of TECHS approach
How to use exchanged information?
Directly be included in the search state of an agent.
Or it can influence the control the agent employs in its search. (as parameters of the control)
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ENGG*6140 Optimization for Engineering

15. ENGG*6140 Optimization for Engineering
Introduction of TECHS approach
Summary of building a TECHS-based search team
Data format for the different types of information must be found.
The used search systems must be modified to provide send-referees with information.
The search systems must also be modified to enable the integration of the information selected by their receive-referees into search state and search control. (very hard)
Send- and receive-referees must be developed.
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ENGG*6140 Optimization for Engineering

16. ENGG*6140 Optimization for Engineering
Introduction of JSSP
Objective: To solve JSSP (Job-Shop Scheduling problem)
Method: TECHS GA GA GA
Branch-and-bound
Branch-and-bound GA GA
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ENGG*6140 Optimization for Engineering

17. ENGG*6140 Optimization for Engineering
Introduction of JSSP
Definition: There are n jobs and m machines; each job comprises a set of operations which must each be done on a different machine for different specified time.
Features:
very important practical problem
being among the worst members of class of NP-complete problems.
It is hard for conventional search-based methods to find near-optima in reasonable time.
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18. ENGG*6140 Optimization for Engineering
Introduction of JSSP
An example
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19. ENGG*6140 Optimization for Engineering
Introduction of JSSP
Existed methods to solve JSSP:
Job-Shop System NY
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20. ENGG*6140 Optimization for Engineering
Introduction of JSSP
Jop-Shop System:
B&B system for solving the JSS problem, implement in C.
B&B based search uses and-trees to represent search states. Transitions :
close leaves in such a tree, if they represent a solution or have a bound that is not better than the best currently known solution.
expand one leaf of the current tree by adding new leaves.
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ENGG*6140 Optimization for Engineering

21. ENGG*6140 Optimization for Engineering
Introduction of JSSP NY
NY is a GA based on the idea of Nakano and Yamada which employs a special version of the Giffler-Thompson algorithm both to create schedules and in the crossover and mutation operators.
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ENGG*6140 Optimization for Engineering

22. ENGG*6140 Optimization for Engineering
TECHS Approach
Central data format: Partial Schedule
Full Schedule & Partial Schedule:
Full schedule: contains for each operation of each job the exact start time and that fulfills all requirements on the problem.
Partial schedule: consists of a set of ordering conditions that are based on two relations between operations on a machine.
op1 > op2: op1 is performed before op2 (maybe other operations between them)
op1 op2: op2 directly follows op1.
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ENGG*6140 Optimization for Engineering

23. ENGG*6140 Optimization for Engineering
TECHS Approach
The Send-Referees
Select information of all types out of the current search state and the search sequence produced so far.
Mainly base their selection on the success the pieces of information produced by the agent.
Receiving agents determine the type of the send-referee.
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ENGG*6140 Optimization for Engineering

24. ENGG*6140 Optimization for Engineering
TECHS Approach
B&BB&B
B&B agents exchange positive and negative information to be integrated into the search state.
Send-referee always selects the best known full schedule as the positive information, if there has been some change since the last cooperation phase.
Negative information are partial schedules describing closed subtrees of the current state. The more nodes this subtree has, the more important it is to communicate this information.
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ENGG*6140 Optimization for Engineering

25. ENGG*6140 Optimization for Engineering
TECHS Approach
B&BNY
Agents using a GA can only use positive information to be included into the search state.
Send-referee selects the partial schedules that have the best bound-values as the positive information(and have not been communicated already).
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ENGG*6140 Optimization for Engineering

26. ENGG*6140 Optimization for Engineering
TECHS Approach
NYB&B
Agents using a GA can only produce positive information.
B&B agents can use the information both as the control information and as information to be integrated into the search state.
The positive information to be integrated into the search state is the best individual of the NY agent.
Positive control information are individuals representing very good solutions. In addition, they should represent different areas of the search tree of the receiving agent than its currently focused area(they should very different).
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27. Positive control information
Solution from GA 125
130
145
130
130
150
150
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28. ENGG*6140 Optimization for Engineering
TECHS Approach
NYNY
The only type of information exchanged are positive individuals to be integrated into the current search state.
Criteria for the selection are quality of the solutions and their difference.
This send-referee was not used in the experiments.
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ENGG*6140 Optimization for Engineering

29. ENGG*6140 Optimization for Engineering
TECHS Approach
The receive-referees
Filter incoming information in order to select information that meet the current needs of their search agents.
NY Agent
Only receives partial schedules to be integrated into its search state.
receive-referee extend partial schedules to full ones.
Criteria used for selection are quality and difference.
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ENGG*6140 Optimization for Engineering

30. ENGG*6140 Optimization for Engineering
TECHS Approach
B&B Agent
Receive-referee filter positive and negative information to be integrated and positive control information.
Positive full schedules : only the one with the highest quality that is higher than the quality of the best known solution so far is selected.(How does the agent use this info?)
Negative partial schedules: used by the agent to close a leaf.
How?
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ENGG*6140 Optimization for Engineering

31. ENGG*6140 Optimization for Engineering
Experiments
Benchmarks stemming from the OR-library.(URL:
Each experiment, maximal run-time : seconds(8 hours)
Working phases:125 seconds.
Communication was synchronized using the function of the UNIX socket concept.
GA agent used 2600 population size.
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ENGG*6140 Optimization for Engineering

32. Solution quality comparison of the single agents Vs TECHS team
Ben. NY
B&B1
B&B2
TECHS
Opt.
La 26
1306
1254
1218
La 27
1300
1330
1298
1271
1235
La 28
1333
1329
1251
1216
La 29
1249
1241
1258
1228
1142
La 36
1323
1304
1291
1268
La 37
1454
1459
1440
1397
La 38
1273
1248
1259
1223
1196
La 39
1285
1264
1233
La 40
1277
1355
1222
swv02
1655
1613
1697
1564
1475
swv05
1668
1660
1622
1600
1421
20*10
15*15
20*10
TECH team produced better schedules than all its agents working alone. The team is always better than its best member.
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ENGG*6140 Optimization for Engineering

33. Comparison of times needed to find comparable solutions
Ben.
Best agent
TECHS team
Speed
-up
quality
time
La 26
1218
8874
4416 2
La 27
1298
24444
1296
1608 15
La 28
1300
2952
817
3.6
La 29
1241
25936
1239
2767
9.3
La 36
1291
12342
2104
5.8
La 37
1440
26180
1426
469 55
La 38
1248
14822
7240
La 39
1249
24474
1245
2516
9.7
La 40
1277
2757
1273
1403
1.9
swv02
1613
6609
6606 1
swv05
1622
22563
1616
7975
2.8
In most case, ETCHS produce comparable solution in less time.
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ENGG*6140 Optimization for Engineering

34. ENGG*6140 Optimization for Engineering
Related Approaches
Most parallelization approaches based on some cooperation of search agents employ homogeneous agents.
Injection island GA
TEAMWORK
Only a few approaches employ search agents with different search models(heterogeneous agents).
TECHS
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35. ENGG*6140 Optimization for Engineering
Conclusion
TECHS presented a cooperation of evolution algorithms with systems on other search paradigms.
TECHS exchange different types of information between agents.
Send/Receive-referees reduces the amount of communication while still selecting the important information.
TECHS results in synergetic improvements both in quality of the solutions found and time needed to find solutions of a certain quality.
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36. ENGG*6140 Optimization for Engineering
Question?
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37. Positive full schedule for B&B agent
Solution from outside: 143
148
135
140
150
150
145
150
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38. Negative Information for B&B Agent
150
148
100
110
135
148
150
140
145
2019/4/24
ENGG*6140 Optimization for Engineering