The Artificial Bee Colony (ABC) is one of the numerous stochastic algorithms\nfor optimization that has been written for solving constrained and unconstrained\noptimization problems. This novel optimization algorithm is very efficient\nand as promising as it is; it can be favourably compared to other optimization\nalgorithms and in some cases, it has been proven to be better than\nsome known algorithms (like Particle Swarm Optimization (PSO)), especially\nwhen used in Well placement optimization problems that can be encountered\nin the Petroleum industry. In this paper, the ABC algorithm has been modified\nto improve its speed and convergence in finding the optimum solution to\na well placement optimization problem. The effects of variations of the control\nparameters for both algorithms were studied, as well as the algorithms�\nperformances in the cases studied. The modified ABC (MABC) algorithm\ngave better results than the Artificial Bee Colony algorithm. It was noticed\nthat the performance of the ABC algorithm increased with increase in the\nnumber of its optimization agents for both algorithms studied. The modified\nABC algorithm overcame the challenge posed by the use of uniformly generated\nrandom numbers with very rough NPV surface. This new modified ABC\nalgorithm proposed in this work will be a great tool in optimization for the\nPetroleum industry as it involves Well placements for optimum oil production.
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