Many heuristic optimization approaches have been developed to combat the ever-increasing complexity of engineering problems. In general, these approaches can be classified based on the diversity of the search strategies used, the amount of change in these search strategies during the optimization process, and the level of cooperation between these strategies. A review of the literature indicates that approaches that are simultaneously very diverse, highly dynamic, and cooperative are rare but have immense potential for finding high quality final solutions. In this work, a taxonomy of heuristic optimization approaches is introduced and used to motivate a new approach called protocol-based multi-agent systems. This approach is found to produce final solutions of much higher quality when its implementation includes the use of multiple search protocols, the adaptation of these protocols during the optimization, and the cooperation between these protocols than when these characteristics are absent.

1.
Haupt
,
R. L.
, and
Haupt
,
S. E.
, 2004,
Practical Genetic Algorithms
,
Wiley
,
Hoboken, NJ
.
2.
Geem
,
Z. W.
, 2009, “
Music-Inspired Harmony Search Algorithm: Theory and Applications
,”
Studies in Computational Intelligence Series
,
Springer-Verlag
,
Berlin
.
3.
Thierens
,
D.
, 2002, “
Adaptive Mutation Rate Control Schemes in Genetic Algorithms
,”
Proceedings of the 2002 IEEE World Congress on Computational Intelligence: Congress on Evolutionary Computation
.
4.
Cantu-Paz
,
E.
, 1997, “
A Survey of Parallel Genetic Algorithms
,” University of Illinois at Urbana-Champaign, Technical Report No. 97003.
5.
Gomes
,
C. P.
, and
Selman
,
B.
, 2001, “
Algorithm Portfolios
,”
Artif. Intell.
0004-3702,
126
, pp.
43
62
.
6.
Kirkpatrick
,
S.
,
Gelatt
,
C. D.
, and
Vecchi
,
M. P.
, 1983, “
Optimization by Simulated Annealing
,”
Science
0036-8075,
220
, pp.
671
680
.
7.
Torczon
,
V.
, and
Trosset
,
M. W.
, 1998, “
From Evolutionary Operation to Parallel Direct Search: Pattern Search Algorithms for Numerical Optimization
,”
Computing Science and Statistics
,
29
, pp.
396
401
.
8.
Leyton-Brown
,
K.
,
Nudelman
,
E.
,
Andrew
,
G.
,
McFadden
,
J.
, and
Shoham
,
Y.
, 2003, “
Boosting as a Metaphor for Algorithm Design
,”
Lect. Notes Comput. Sci.
0302-9743,
2833
, pp.
899
903
.
9.
Petrik
,
M.
, and
Zilberstein
,
S.
, 2006, “
Learning Parallel Portfolios of Algorithms
,”
Ann. Math. Artif. Intell.
1012-2443,
48
, pp.
85
106
.
10.
Streeter
,
M.
,
Golovin
,
D.
, and
Smith
,
S. F.
, 2007, “
Combining Multiple Heuristics Online
,”
Proceedings of the 22nd Conference on Artificial Intelligence
, Vol.
2
, pp.
1197
1203
.
11.
Rachlin
,
J.
,
Goodwin
,
R.
,
Murthy
,
S.
,
Akkiraju
,
R.
,
Wu
,
F.
,
Kumaran
,
S.
, and
Das
,
R.
, 1999, “
A-Teams: An Agent Architecture for Optimization and Decision Support
,”
Lect. Notes Comput. Sci.
0302-9743,
1555
, pp.
261
276
.
12.
DeSouza
,
P. S.
, and
Talukdar
,
S. N.
, 1993, “
Asynchronous Organizations for Multi-Algorithm Problems
,”
Proceedings of the 1993 ACM/SIGAPP Symposium on Applied Computing: States of the Art and Practice
, pp.
286
293
.
13.
Campbell
,
M. I.
,
Cagan
,
J.
, and
Kotovsky
,
K.
, 1999, “
A-Design: An Agent-Based Approach to Conceptual Design in a Dynamic Environment
,”
Res. Eng. Des.
0934-9839,
11
, pp.
172
192
.
14.
Back
,
T. D.
,
Hoffmeister
,
F.
, and
Schwefel
,
H. P.
, 1991, “
A Survey of Evolution Strategies
,”
Proceedings of the Fourth International Conference on Genetic Algorithms
.
15.
Pohlheim
,
H.
, 2001, “
Competition and Cooperation in Extended Evolutionary Algorithms
,”
Proceedings of the Genetic and Evolutionary Computation Conference
.
16.
Huberman
,
B. A.
,
Lukose
,
R. M.
, and
Hogg
,
T.
, 1997, “
An Economics Approach to Hard Computational Problems
,”
Science
0036-8075,
275
, pp.
51
54
.
17.
Fogarty
,
T.
, 1989, “
Varying the Probability of Mutation in the Genetic Algorithm
,”
Proceedings of the Third International Conference on Genetic Algorithms
, pp.
104
109
.
18.
Hesser
,
J.
, and
Manner
,
R.
, 1991, “
Towards an Optimal Mutation Probability in Genetic Algorithms
,”
Proceedings of the First Parallel Problem Solving From Nature
.
19.
Hesser
,
J.
, and
Manner
,
R.
, 1992, “
Investigation of the M-Heuristic for Optimal Mutation Probabilities
,”
Proceedings of the Second Parallel Problem Solving From Nature
.
20.
Back
,
T.
, and
Schutz
,
M.
, 1996, “
Intelligent Mutation Rate Control in Canonical Genetic Algorithms
,”
Proceedings of the International Symposium on Methodologies for Intelligent Systems
.
21.
Yin
,
S.
, and
Cagan
,
J.
, 2000, “
An Extended Pattern Search Algorithm for Three-Dimensional Component Layout
,”
ASME J. Mech. Des.
0161-8458,
122
, pp.
102
108
.
22.
Hustin
,
S.
, and
Sangiovanni-Vincentelli
,
A.
, 1987, “
TIM, a New Standard Cell Placement Program Based on the Simulated Annealing Algorithm
,”
IEEE Physical Design Workshop on Placement and Floorplanning
.
23.
Gagliolo
,
M.
, and
Schmidhuber
,
J.
, 2004, “
Adaptive Online Time Allocation to Search Algorithms
,”
Lect. Notes Comput. Sci.
0302-9743,
3201
, pp.
134
143
.
24.
Gagliolo
,
M.
, and
Schmidhuber
,
J.
, 2007, “
Learning Dynamic Algorithm Portfolios
,” Technical Report No. IDSIA-02-07.
25.
Moral
,
R. J.
,
Sahoo
,
D.
, and
Dulikravich
,
G. S.
, 2006, “
Multi-Objective Hybrid Evolutionary Optimization With Automatic Switching
,”
11th AIAA Multidisciplinary Analysis and Optimization Conference
.
26.
Moral
,
R. J.
, and
Dulikravich
,
G. S.
, 2008, “
Multi-Objective Hybrid Evolutionary Optimization With Automatic Switching Among Constituent Algorithms
,”
AIAA J.
0001-1452,
46
, pp.
673
681
.
27.
Dorigo
,
M.
, and
Stutzle
,
T.
, 2004,
Ant Colony Optimization
,
MIT
,
Cambridge, MA
.
28.
Huang
,
M. D.
,
Romeo
,
F.
, and
Sangiovanni-Vincentelli
,
A.
, 1986, “
An Efficient General Cooling Schedule for Simulated Annealing
,”
IEEE International Conference on Computer Aided Design—Digest of Technical Papers
, pp.
381
384
.
29.
Smith
,
J.
, and
Fogarty
,
T. C.
, 1997, “
Operator and Parameter Adaptation in Genetic Algorithms
,”
Soft Comput.
1432-7643,
1
, pp.
81
87
.
30.
Szykman
,
S.
, and
Cagan
,
J.
, 1995, “
A Simulated Annealing-Based Approach to Three-Dimensional Component Packing
,”
ASME J. Mech. Des.
0161-8458,
117
, pp.
308
314
.
31.
Hoffmeister
,
F.
, and
Back
,
T.
, 1991, “
Genetic Algorithms and Evolution Strategies: Similarities and Differences
,”
Lect. Notes Comput. Sci.
0302-9743,
496
, pp.
455
469
.
32.
Cantú-Paz
,
E.
, 2001, “
Migration Policies, Selection Pressure, and Parallel Evolutionary Algorithms
,”
J. Heuristics
1381-1231,
7
, pp.
311
334
.
33.
Talukdar
,
S.
,
Baerentzen
,
L.
,
Gove
,
A.
, and
DeSouza
,
P.
, 1998, “
Asynchronous Teams: Cooperation Schemes for Autonomous Agents
,”
J. Heuristics
1381-1231,
4
, pp.
295
321
.
34.
Vrugt
,
J. A.
, and
Robinson
,
B. A.
, 2007, “
Improved Evolutionary Optimization From Genetically Adaptive Multi-Method Search
,”
Proc. Natl. Acad. Sci. U.S.A.
0027-8424,
104
, pp.
708
711
.
35.
Nii
,
H. Y.
, 1986, “
Blackboard Systems
,” Stanford University, Technical Report No. STAN-CS-86-1123/KSL-86-18.
36.
Hanna
,
L.
, and
Cagan
,
J.
, 2008, “
Evolutionary Multi-Agent Systems, an Adaptive Approach to Optimization in Dynamic Environments
,”
Proceedings of the ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference IDETC/CIE
.
37.
Hanna
,
L.
, and
Cagan
,
J.
, 2009, “
Evolutionary Multi-Agent Systems: An Adaptive and Dynamic Approach to Optimization
,”
ASME J. Mech. Des.
0161-8458,
131
, p.
011010
.
38.
Aladahalli
,
C.
,
Cagan
,
J.
, and
Shimada
,
K.
, 2007, “
Objective Function Effect Based Pattern Search—Theoretical Framework Inspired by 3D Component Layout
,”
ASME J. Mech. Des.
0161-8458,
129
, pp.
243
254
.
39.
Hohn
,
C.
, and
Reeves
,
C.
, 1996, “
The Crossover Landscape for the Onemax Problem
,”
Proceedings of the Second Nordic Workshop on Genetic Algorithms
.
40.
Mortensen
,
M. E.
, 1997,
Geometric Modeling
,
Wiley
,
New York
.
You do not currently have access to this content.