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...

Hybrid sorting immune simulated annealing technique (HSISAT), a Meta heuristic is proposed for solving the multi objective flexible job-shop scheduling problem (FJSP). The major objectives are distributing the time of machines among the set of operations and scheduling them to minimize the criterion (makespan, total workload and maximum workload). The processing time is sorted for isolating the...

In computer-aided logic design, partitioning is the task of clustering circuit elements into groups so that a given objective function is optimized with respect to a set of design constraints. In this paper we present a genetic algorithm for the circuit partitioning problem. The objective of the proposed algorithm is not only to balance the size of the two portions, but also to evenly distribut...

It is difficult to determine the appropriate temperature parameters which control the acceptance probability in Simulated Annealing, which is a typical meta-heuristic method in the optimization methods. In this paper, we propose a new simulated annealing method that determines the maximum temperature adaptively. The proposed method is base on an important temperature where optimum solutions wer...

Operations flexibility refers to production environments where there are more tasks than workstations and any task can be assigned to any workstation. This article investigates the advantages of operations flexibility in a flowshop when the objectives are to minimize the makespan and workstation utilization. The proposed heuristic framework consists of two phases, allocation of operations to wo...

In this paper we are concerned with finding the Pareto optimal front or a good approximation to it. Since non-dominated solutions represent the goal in multiobjective optimisation, the dominance relation is frequently used to establish preference between solutions during the search. Recently, relaxed forms of the dominance relation have been proposed in the literature for improving the performa...

We combine concepts from numerous papers to provide a derivation and description of a generalized Probabilistic Multi-Hypothesis Tracker that can track multiple targets in a cluttered environment, utilizing multiple sensors and feature measurements, if available. Additionally, we provide a full derivation of the algorithm, including parts omitted or abbreviated in other work. We also provide an...

Adaptive simulated annealing (ASA) is a global optimization algorithm based on an associated proof that the parameter space can be sampled much more efficiently than by using other previous simulated annealing algorithms. The author’s ASA code has been publicly available for over two years. During this time the author has volunteered to help people via e-mail, and the feedback obtained has been...

Stochastic global optimization is a very important subject, that has applications in virtually all areas of science and technology. Therefore there is nothing more opportune than writing a book about a successful and mature algorithm that turned out to be a good tool in solving difficult problems. Here we present some techniques for solving several problems by means of Fuzzy Adaptive Simulated ...

We have previously developed an adaptation of the simulated annealing for multi-objective combinatorial optimization (MOCO) problems to construct an approximation of the e$cient set of such problem. In order to deal with large-scale problems, we transform this approach to propose an interactive procedure. The method is tested on the multi-objective knapsack problem and the multi-objective assig...