This paper introduces an effective production optimization and a water injection allocation method for oil reservoirs with water injection. In this method, a two-stage adaptive simulated annealing (ASA) is used. A coarse-grid model is made based on average horizon permeability at the beginning iterations of the optimization to search quickly. In the second stage, the fine-grid model is used to ...

A fully-automatic approach to volume registration based on global information content is described. In a nutshell this task represents an optimization problem that has been solved with respect to robustness and computational speed especially. Strong attention has been paid to the choice of an optimized function that measure the quality of the registration (Mutual Information), to the choice of ...

A new heuristic approach for minimizing possibly nonlinear and non differentiable continuous space functions is presented. By means of an extensive testbed, which includes the De Jong functions, it is demonstrated that the new method converges faster and with more certainty than both Adaptive Simulated Annealing and the Annealed Nelder&Mead approach. The new method requires few control variable...

A guided stochastic search algorithm, known as the repeated weighted boosting search (RWBS), offers an effective means for solving the difficult single-objective optimisation problems with non-smooth and/or multi-modal cost functions. Compared with other global optimisation solvers, such as the genetic algorithms (GAs) and adaptive simulated annealing, RWBS is easier to implement, has fewer alg...

This work has attempted to exploit information sharing to improve the results of Adaptive Simulated Annealing [1] as an optimization algorithm of the high-level synthesis of testable data paths. We have used Messengers [3] as a coordination tool to run several parallel instances of the annealing algorithm on the same design with di erent probability arrays for the perturbations. When all these ...

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...

Abstract – Simulated annealing (SA) is an effective general heuristic method for solving many optimization problems. This paper deals with the two problems in SA. One is the long computational time of the numerical annealings, and the solution to it is the parallel processing of SA. The other one is the determination of the appropriate neighborhood range in SA, and the solution to it is the int...

A multi-objective optimization problem can be solved by decomposing it into one or more single objective subproblems in some multi-objective metaheuristic algorithms. Each subproblem corresponds to one weighted aggregation function. For example, MOEA/D is an evolutionary multi-objective optimization (EMO) algorithm that attempts to optimize multiple subproblems simultaneously by evolving a popu...

Bayesian belief networks (BBN’s) are a popular graphical representation for reasoning under (probabilistic) uncertainty. An important, and NP-complete, problem on BBN’s is the maximum a posteriori (MAP) assignment problem, in which the goal is find the network assignment with highest conditional probability given a set of observances, or evidence. In this paper, we present an adaptive hybrid te...

This paper presents a new hybrid evolutionary algorithm to solve multi-objective multicast routing problems in telecommunication networks. The algorithm combines simulated annealing strategies and genetic local search, aiming at a more flexible and effective exploration and exploitation in the search space of the complex problem to find more non-dominated solutions in the Pareto Front. Due to t...