求解经济负荷分配问题的改进适应度相关优化算法
Improved Fitness Dependent Optimizer for Solving Economic Load Dispatch Problem
摘要：
经济负荷分配在电力系统的运行中起着重要的作用，因为它降低了环境负荷，最大限度地减少了运行成本，并节省了能源。经济负荷分配问题的最优解和各种约束条件可以通过进化算法和基于群体的算法来获得。基于群的算法的主要缺点是过早收敛到最优解。适应度相关优化算法是受蜂群决策和繁殖过程启发而提出的一种新的优化算法。适应度相关优化算法（FDO）基于粒子群优化算法的搜索方法来检查搜索空间。为了计算速度，利用适应度函数来生成在开发和探索阶段指导搜索代理的权重。在这项研究中，作者进行了适应度依赖优化解决经济负荷分配问题，减少燃料成本，排放分配，输电损耗。此外，作者还增强了一种新的变体的健身依赖优化，它结合了新的人口初始化技术，并动态采用正弦映射来选择权重因子的健身依赖优化。该算法在多维搜索空间中引入准随机Sabol序列来产生初始解。一个标准的24个单位的系统是采用不同的功率需求的实验评估。经验结果表明，使用增强的健身依赖优化的变体表现出优越的性能，在低传输损耗，低燃料成本，低排放分配相比，传统的健身依赖优化。实验研究获得7.94E-12。
英文摘要：
Economic Load Dispatch depicts a fundamental role in the operation of power systems, as it decreases the environmental load, minimizes the operating cost, and preserves energy resources. The optimal solution to Economic Load Dispatch problems and various constraints can be obtained by evolving several evolutionary and swarm-based algorithms. The major drawback to swarm-based algorithms is premature convergence towards an optimal solution. Fitness Dependent Optimizer is a novel optimization algorithm stimulated by the decision-making and reproductive process of bee swarming. Fitness Dependent Optimizer (FDO) examines the search spaces based on the searching approach of Particle Swarm Optimization. To calculate the pace, the fitness function is utilized to generate weights that direct the search agents in the phases of exploitation and exploration. In this research, the authors have carried out Fitness Dependent Optimizer to solve the Economic Load Dispatch problem by reducing fuel cost, emission allocation, and transmission loss. Moreover, the authors have enhanced a novel variant of Fitness Dependent Optimizer, which incorporates novel population initialization techniques and dynamically employed sine maps to select the weight factor for Fitness Dependent Optimizer. The enhanced population initialization approach incorporates a quasi-random Sabol sequence to generate the initial solution in the multi-dimensional search space. A standard 24-unit system is employed for experimental evaluation with different power demands. Empirical results obtained using the enhanced variant of the Fitness Dependent Optimizer demonstrate superior performance in terms of low transmission loss, low fuel cost, and low emission allocation compared to the conventional Fitness Dependent Optimizer. The experimental study obtained 7.94E-12.
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