A new approach to combinatorial optimization based on systematic move– class deflation is proposed. The algorithm combines heuristics of genetic algorithms and simulated annealing, and is mainly entropy–driven. It is tested on two problems known to be NP hard, namely the problem of finding ground states of the SK spin–glass and of the 3-D ±J spin–glass. The algorithm is sensitive to properties ...

1 2 Summary Simulated annealing can minimize both proole and ll of sparse matrices. We applied these techniques to a number of sparse matrices from the Harwell-Boeing Sparse Matrix Collection. We were able to reduce proole typically to about 80% of that attained by conventional proole minimization techniques (and sometimes much lower), but ll reduction was less successful (85% at best). We pres...

The Network Design Problem (NDP) is one of the important problems in combinatorial optimization. Among the network design problems, the Multicommodity Capacitated Network Design (MCND) problem has numerous applications in transportation, logistics, telecommunication, and production systems. The MCND problems with splittable flow variables are NP-hard, which means they require exponential time t...

Job Shop Scheduling Problem (JSSP) is an optimization problem in which ideal jobs are assigned to resources at particular times. In recent years many attempts have been made at the solution of JSSP using a various range of tools and techniques such as Branch and Bound and Heuristics algorithms. This paper proposed a new algorithm based on Genetic Algorithm (GA), Tabu Search (TS) and Simulated A...

An investigational setup had been made up using a cooling box, gas burner, LPG capillary tubes, and pressure controller. The running time, internal coil diameters of tubes were measured as operating parameters. best regression model has built from the successive sum square approach. It is used for simulated annealing algorithm (SA). effort done scheme was also enhanced by long working period. e...

This paper presents a new method to pack convex polygons into bins (the nesting problem). To do this polygons are placed in rows within bins using a metaheuristic algorithm (simulated annealing) and by utilising the No Fit Polygon. We show that simulated annealing out performs hill climbing. The nesting algorithm is described in detail, along with various aspects that have been incorporated in ...