In this work, we present a cluster-based learning and evolution optimizer (CLEO) for solving optimization problems. CLEO is a metaheuristic algorithm that uses cluster-based manipulation of the problem space during th...
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In this work, we present a cluster-based learning and evolution optimizer (CLEO) for solving optimization problems. CLEO is a metaheuristic algorithm that uses cluster-based manipulation of the problem space during the exploration phase, followed by fine-tuning solutions in the exploitation phase using updated knowledge of the problem space. We propose two approaches based on this new algorithm: one using only Latin hypercube sampling (LHS) and the other using LHS in combination with reservoir engineering insights. In addition to ensuring realistic simulation scenarios, we employed intuitive engineering insights to reveal how empirical knowledge enhances efficiency. Also, we propose simulating the partial life instead of the complete lifespan in the second approach. Technical results obtained at the end of this period are processed and used to find the optimized field development plan (FDP). We conducted both deterministic and probabilistic studies to assess the performance of the proposed approaches for various decision variables, both numerous and restricted. We validated the algorithm by optimizing the FDP for a simple numerical simulation model and a giant field-scale model, and compared our approaches to four well-established optimizers (particle swarm optimization (PSO), differential evolution (DE), designed exploration controlled evolution (DECE), and iterative discrete Latin hypercube sampling method (IDLHC)) in terms of simulation time and objective function results. Overall, the comparison demonstrates the advantages of the newly proposed algorithm. The results indicate that our first approach performs as well as any well-established optimizer, notably when working with large scale optimization problems. The second approach has slightly lower objective function results than the first one, but it is the most efficient among the compared algorithms, as the best FDP can be obtained by covering as little as 40% of the field's life. This attribute makes it an e
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