The Artificial Bee Colony (ABC) is one of the numerous stochastic algorithms for optimization that has been written for solving constrained and unconstrained optimization problems. This novel optimization algorithm is very efficient and as promising as it is;it can be favourably compared to other optimization algorithms and in some cases, it has been proven to be better than some known algorithms (like Particle Swarm Optimization (PSO)), especially when used in Well placement optimization problems that can be encountered in the Petroleum industry. In this paper, the ABC algorithm has been modified to improve its speed and convergence in finding the optimum solution to a well placement optimization problem. The effects of variations of the control parameters for both algorithms were studied, as well as the algorithms’ performances in the cases studied. The modified ABC (MABC) algorithm gave better results than the Artificial Bee Colony algorithm. It was noticed that the performance of the ABC algorithm increased with increase in the number of its optimization agents for both algorithms studied. The modified ABC algorithm overcame the challenge posed by the use of uniformly generated random numbers with very rough NPV surface. This new modified ABC algorithm proposed in this work will be a great tool in optimization for the Petroleum industry as it involves Well placements for optimum oil production.
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机译：两岸四地累犯制度比较研究——兼论中国内地累犯制度一体化之构想 =Comparative Study on Recidivism System in Hong Kong, Macao, Taiwan and China: Concurrently Discuss the Conception of Recidivism System Integration in Mainland China
机译：“三位一体”综合评价招生制度的录取生与高考正取生学习适应性的比较研究Comparative Study of Study Adaptability of Admitted Students between “The Triplicity” Enrollment System and the College Entrance Examination
