By Cesar Rego, Bahram Alidaee
Tabu seek (TS) and, extra lately, Scatter seek (SS) have proved powerful in fixing a variety of optimization difficulties, and feature had numerous functions in undefined, technology, and govt. The target of Metaheuristic Optimization through reminiscence and Evolution: Tabu seek and Scatter seek is to document unique learn on algorithms and functions of tabu seek, scatter seek or either, in addition to diversifications and extensions having "adaptive reminiscence programming" as a major concentration. person chapters determine priceless new implementations or new how one can combine and observe the rules of TS and SS, or that end up new theoretical effects, or describe the winning software of those tips on how to genuine international difficulties.
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Additional info for Metaheuristic Optimization via Memory and Evolution: Tabu Search and Scatter Search (Operations Research Computer Science Interfaces Series)
For example, the previous linear combination produced the following solution fragments: (1,8,0), (2,0), (0,3,4) (0,6,5,13,14,0), (0,11,9,10,12,0), and (7,0). To create a feasible solution subgraph we can simply link vertices 1, 2, 4, and 7 (which have a degree equal to 1) directly to the depot. This has the advantage that the algorithm always deals with feasible structures. However, it is also possible that the subgraph resulting from a linear combination contains vertices of degree greater than two.
1. Characteristics of Floudas GAMS test problems 14 4 9 2 2 3 4 10 4 6 8 5 14 8 6 N from 4 to 147 N from 5 to 80 10 9 6 9 9 142 24 8 2 32 62 27 12 9 8 17 6 55 141 62 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 10 4 0 8 19 13 3 3 3 10 0 6 43 0 2 0 6 2 11 34 6 6 0 12 11 5 75 65 40 2 concave QP (min) quadratic obj and constraints obj or constraints polynomial bilinear-pooling distillation column sequencing heat exchanger network gibbs free energy min gibbs free energy min generalized geometric prog robust stability analysis small unconstrained, constrained batch plant design-uncertainty reactor network synthesis constrained least squares min tangent plane distance 3N 3N 29 16 11 10 7 0 0 0 0 8 0 0 0 0 27 11 9 4 1 0 0 5 6 4 17 10 Lenard-Jones energy min Morse energy min bilevel LP bilevel QP MINLP infinity norm solution of equations infinity norm solution of equations Computational Results on the Floudas Set of Test Problems This section describes the results obtained when the OQNLP algorithm described in Section 6 is applied to the Floudas GAMS test problems.
This uses a simple rule to exclude some potential starting points. A uniformly distributed sample of N points in S is generated, and the objective,/ is evaluated at each point. The points are sorted according to their / values, and the qN best points are retained, where q is an algorithm parameter between 0 and 1. L is started from each point of this reduced sample, except if there is another sample point within a certain critical distance which has a lower/value. L is also not started from sample points which are too near the boundary of S, or too close to a previously discovered local minimum.