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Ta b l e 1 . 3 . Building blocks and the enhanced versions of DE
Building Blocks
Enhanced DE versions
Chapter
Forward/Backward Transformation Approach
Enhanced DE (EDE)
3
Relative Position Indexing Approach
HPS
4
Smallest Position Value Approach
-
5
Discrete/Binary Approach
-
6
Discrete Set Handling Approach
DE DSH
7
1. Scheduling: Flow Shop, Job Shop, etc.
2. Knapsack
3. Linear Assignment Problem (LAP)
4. Quadratic Assignment Problem (QAP)
5. Traveling Salesman Problem (TSP)
6. Vehicle Routine Problem (VRP)
7. Dynamic pick-and-place model of placement sequence and magazine assignment
in robots
In this topic, some methods for realizing DE for permutative-based combinatorial
optimization problems that are presented in succeeding chapters are as follows:
1. Forward/Backward Transformation Approach: [chapter 3];
2. Relative Position Indexing Approach: [chapter 4];
3. Smallest Position Value Approach: [chapter 5];
4. Discrete/Binary Approach: [chapter 6]; and
5. Discrete Set Handling Approach: [chapter 7].
listed foundations have been presented in the topic, it should be
mentioned that a number of enhancement routines have been realized which are based
on these fundamental building blocks. For example, the enhanced DE (EDE) is based on
fundamentals of the forward/backward transformation approach presented in chapter 3.
This philosophy threads throughout the topic and should be borne in mind when reading
the chapters. Consequently, we have the building blocks and the enhanced versions of
DE listed in Table 1.3.
While the above
1.4
Conclusions
This chapter has discussed and differentiated both the continuous space DE formulation
and the permutative-based combinatorial DE formulation and shown that these formu-
lations compliment each other and none of them is complete on its own. Therefore
we have shown that this topic complements that of [2] and vice versa. Taken together
therefore, both topics will be needed by practitioners and students interested in DE in
order to have the full potentials of DE at their disposal. In other words, DE as an area
of optimization is incomplete unless it can deal with real-life problems in the areas of
continuous space as well as permutative-based combinatorial domain.
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