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A hyperheuristic optimization technique to reduce computational times for the design of pipeline networks is presented. The proposed strategy is an A-team approach comprising the guided execution of three metaheuristics: a genetic algorithm, simulated annealing, and an ant colony optimization. Besides, a specialized learning mechanism for information exchange was defined in order to speed up the search process. Moreover, the algorithm was implemented in parallel so as to allow several metaheuristics to run simultaneously, thus achieving a significant reduction of time overhead. In the algorithmic design, realistic scenarios were employed so as to appraise the impact of each agent on optimization efficiency. The cases correspond to real-world offshore infrastructures to be located in the Argentinian marine platform. They were also analyzed to illustrate the validity and suitability of the proposed approach. This optimization technique proved to be competitive since it is able to explore a wide search space fast, yielding satisfactory solutions. Fil: Oteiza, Paola Patricia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería Química; Argentina Fil: Rodriguez, Diego Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Laboratorio de Investigación y Desarrollo en Computación Científica; Argentina. Universidad Nacional de Salta; Argentina Fil: Brignole, Nélida Beatriz. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Laboratorio de Investigación y Desarrollo en Computación Científica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina

RS: Have you guys ever heard of kissing rugby? WC: No. RS: It’s hilarious. What you do is all the girls from one long line- everyone is sitting on the ground, and the girls are all given letters of the alphabet “A” though whatever it goes to , and then the boys sit in a long line facing the girls but about 25 feet apart and they all get numbers, “1” to whatever. Then there’s a person that’s sitting in the middle between the 2 lines and they call out a letter and a number. If it’s a girl in the middle the girl letter called tries to kiss the boy number called while the boy number is trying to kiss the girl in the middle who is trying to get back to the open spot of the girl alphabet. Its opposite if it’s a boy in the middle. Then whoever is the person that gets kissed is the person who has to stay in the middle. WC: [various laughter and small comments to each other] RS: So I used to work for the university on the A-Team- do you guys know what that is? WC: [various yes’s from class] RS: So we did a camping trip registration and we were playing kissing rugby and this boy was trying to kiss a girl who was trying to kiss the middle boy and it got really crazy and one of the boys broke his nose! WC: [various laughing and exclamations of surprise] RS: Yep, it was all university sponsored too.

In this paper the dynamic interaction strategy based on the Population Learning Algorithm (PLA) for the A-Team solving the Resource-Constrained Project Scheduling Problem (RCPSP) is proposed and experimentally validated. The RCPSP belongs to the NP-hard problem class. To solve this problem a team of asynchronous agents (A-Team) has been implemented using multiagent system. An A-Team is the set of objects including multiple agents and the common memory which through interactions produce solutions of optimization problems. These interactions are usually managed by some static strategy. In this paper the dynamic learning strategy based on PLA is suggested. The proposed strategy supervises interactions between optimization agents and the common memory. To validate the proposed approach computational experiment has been carried out.