The choice of a good annealing schedule is necessary for good performance of simulated annealing for combinatorial optimization problems. In this paper, we pose the simulated annealing task decision-theoretically for the first time, allowing the user to explicitly define utilities of time and solution quality. We then demonstrate the application of reinforcement learning techniques towards appr...

This paper proposes the application of chaos in large search space problems, and suggests that this represents the next evolutionary step in the development of adaptive and intelligent systems towards cognitive machines and systems. Three different versions of chaotic simulated annealing (XSA) were applied to combinatorial optimization problems in multiprocessor task allocation. Chaotic walks i...

With several commercial tools becoming available, the high-level synthesis of applicationspeci c integrated circuits is nding wide spread acceptance in VLSI industry today. Existing tools for synthesis focus on optimizing cost while meeting performance constraints or vice versa. Yet, veri cation and testing have emerged as major concerns of IC vendors since the repurcussions of chips being reca...

Chaotic simulated annealing (CSA) proposed by Chen and Aihara has been successfully used to solve a variety of combinatorial optimization problems. CSA uses a penalty term to enforce solution validity as in the original Hopfield-Tank approach. There exists a conflict between solution quality and solution validity in the penalty approach. It is often difficult to adjust the relative magnitude of...

In this study we have tackled the NP-hard problem of academic class scheduling (or timetabling) at the university level. We have investigated a variety of approaches based on simulated annealing, including mean-field annealing, simulated annealing with three different cooling schedules, and the use of a rule-based preprocessor to provide a good initial solution for annealing. The best results w...

Quadratic Assignment Problem (QAP) is an NPhard combinatorial optimization problem, therefore, solving the QAP requires applying one or more of the meta-heuristic algorithms. This paper presents a comparative study between Meta-heuristic algorithms: Genetic Algorithm, Tabu Search, and Simulated annealing for solving a real-life (QAP) and analyze their performance in terms of both runtime effici...

This paper is devoted to the approximate solution of a strongly NP -hard resource-constrained scheduling problem which arises in relation to the operability of certain high availability real time distributed systems. We present an algorithm based on the simulated annealing metaheuristic and, building on previous research on exact solution methods, extensive computational results demonstrating i...

We propose a dual sequence Simulated Annealing algorithm, DSAC, for solving constrained optimization problems. This approach eliminates the need for imposing penalties in the objective function by tracing feasible and infeasible solution sequences independently. We compare DSAC with a similar single sequence algorithm, PSAC, where the objective function is augmented by various penalty forms rel...

The paper considers the problem of maximizing the profits of a trader operating in the Italian electricity market. The problem consists in selecting the contracts portfolio and define the bidding strategy in the wholesales market while respecting the technical and regulatory constraints. A novel solution method based on a enhanced discovery of the search domain in the simulated annealing techni...