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Abstract: The objective of the research presented in this paper is to investigate the application of a new metaheuristic algorithm called ant colony algorithm to petroleum engineering problems. This algorithm is usually used for discrete domains, but with some modifications, has been applied to continuous optimization. In this paper, three examples with continuous and discrete parameters as well as known solutions and varying degrees of complexities are presented as an illustration for solving a large class of process optimization problems in petroleum engineering. Results of case studies show the ability of ant colony algorithm to provide fast and accurate solutions. [ABSTRACT FROM AUTHOR]

A hybrid optimization technique, GA–SQP, is developed in which the genetic algorithm (GA) which is a stochastic method is combined with the sequential quadratic programming (SQP) method which is a deterministic method. This method was used to determine the kinetic parameters of the set of highly nonlinear hydrogenation reactions. Catalyst deactivation was also taken into account. The ability of GA and SQP in solving this type of problem was investigated. It was shown that although the SQP is fast, it is not able to solve this problem properly and is very sensitive to the choice of initial point. The GA was able to solve the problem after a large number of generations. It was shown that the new GA–SQP hybrid method is able to determine the final solution considerably faster than the GA while it is not sensitive to the initial point. [Copyright &y& Elsevier]