Abstract

This work proposes a metaphor-less and algorithm-specific parameter-less algorithm, named as self-adaptive population Rao algorithm, for solving the single-, multi-, and many-objective optimization problems. The proposed algorithm adapts the population size based on the improvement in the fitness value during the search process. The population is randomly divided into four sub-population groups. For each sub-population, a unique perturbation equation is randomly allocated. Each perturbation equation guides the solutions toward different regions of the search space. The performance of the proposed algorithm is examined using standard optimization benchmark problems having different characteristics in the single- and multi-objective optimization scenarios. The results of the application of the proposed algorithm are compared with those obtained by the latest advanced optimization algorithms. It is observed that the results obtained by the proposed method are superior. Furthermore, the proposed algorithm is used to identify optimum design parameters through multi-objective optimization of a fertilizer-assisted microalgae cultivation process and many-objective optimization of a compression ignition biodiesel engine system. From the results of the computational tests, it is observed that the performance of the self-adaptive population Rao algorithm is superior or competitive to the other advanced optimization algorithms. The performances of the considered bio-energy systems are improved by the application of the proposed optimization algorithm. The proposed optimization algorithm is more robust and may be easily extended to solve single-, multi-, and many-objective optimization problems of different science and engineering disciplines.

中文翻译：

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用于选择生物能源系统优化的自适应种群Rao算法

摘要

这项工作提出了一种无隐喻和算法特定的无参数算法，称为自适应总体Rao算法，用于解决单目标，多目标和多目标优化问题。所提出的算法根据搜索过程中适应度值的提高来适应人口规模。人口被随机分为四个亚群。对于每个子种群，随机分配一个唯一的摄动方程。每个扰动方程将解导向搜索空间的不同区域。在单目标和多目标优化方案中，使用具有不同特征的标准优化基准问题来检验所提出算法的性能。将该算法的应用结果与最新的高级优化算法获得的结果进行了比较。观察到，通过所提出的方法获得的结果是优异的。此外，该算法可通过肥料辅助微藻培养过程的多目标优化和压燃生物柴油发动机系统的多目标优化来确定最佳设计参数。从计算测试的结果可以看出，自适应总体Rao算法的性能优于或优于其他高级优化算法。所考虑的生物能源系统的性能通过所提出的优化算法的应用得以改善。