In this paper, six different approaches using genetic algorithms (GA) and/or simulated annealing (SA) with improved bottom left (I-BL) algorithm [1] were applied for solution of two dimensional non-guillotine cutting problems. As examples, test problems including 29 individual rectangular pieces were used [2]. Performances of hybrid approaches on solutions of cutting problems were compared. Due...

Simulated annealing (SA) algorithm is a popular intelligent optimisation algorithm, but its efficiency is unsatisfactory. To improve its efficiency, this paper presents a swarm SA (SSA) algorithm by exploiting the learned knowledge from searching history. In SSA, a swarm of individuals run SA algorithm collaboratively. Inspired by ant colony optimisation (ACO) algorithm, SSA stores knowledge in...

In [6] solutions for the nesting problem are produced using the No Fit Polygon (NFP), simulated annealing (SA) and a new evaluation method. It showed that SA could out perform hill climbing, thus suggesting that evolutionary approaches produce better solutions than a standard search algorithm. In this paper this work is developed. Genetic algorithms (GA) and tabu search (TS) are compared with t...

In this paper, we study the unrelated parallel machine problem for minimizing the makespan, which is NP-hard. We used Simulated Annealing (SA) and Tabu Search (TS) with Neighborhood Search (NS) based on the structure of the problem. We also used a modified SA algorithm, which gives better results than the traditional SA and developed an effective heuristic for the problem: Squeaky Wheel Optimiz...

The simulated annealing (SA) algorithm [14] [5] has been applied to every di cult optimization problem in VLSI CAD. Existing SA implementations use monotone decreasing, or cooling, temperature schedules motivated by the algorithm's proof of optimality as well as by an analogy with statistical thermodynamics. This paper gives strong evidence that challenges the correctness of using such schedule...

In this paper, we propose a new probabilistic sampling procedure and its application in simulated annealing (SA). The new procedure uses Bayesian analysis to evaluate samples made already and draws the next sample based on a density function constructed through Bayesian analysis. After integrating our procedure in SA, we apply it to solve a set of optimization benchmarks. Our results show that ...

The Genetic Algorithm (GA) and Simulated Annealing (SA), two techniques for global optimization, were applied to a reduced (simplified) form of the phase problem (RPP) in computational crystallography. Results were compared with those of "enhanced pair flipping" (EPF), a more elaborate problem-specific algorithm incorporating local and global searches. Not surprisingly, EPF did better than the ...

Most of graph drawing algorithms draw graphs on unbounded planes. In this paper we introduce a new polyline grid drawing algorithm for drawing free trees on plane regions which are bounded by simple polygons. Our algorithm uses the simulated annealing (SA) method, and by means of the straight skeletons of the bounding polygons guides the SA method to uniformly distribute the vertices of the giv...

In this paper we propose a new hybrid algorithm for solving the Unit Commitment Problem (UCP). The algorithm integrates the use of Simulated Annealing (SA) and Tabu Search (TS) for solving the UCP. The algorithm includes a step to find an initial control parameter, at which virtually all trial solutions are accepted. It uses a polynomial-time cooling schedule that is advocated in the SA literat...

Sometimes, it is difficult to cope with a good set of values for the parameters of an algorithm that solves an specific optimization problem. This work presents a methodology for fine-tuning the parameters of a Simulated Annealing (SA) algorithm solving the Bridge Club (BC) problem. The methodology uses Covering Arrays as a tool that evaluates a set of values for the parameters of the SA so tha...