Improved Fitness Dependent Optimizer for Solving Economic Load Dispatch Problem

TL;DR

This paper introduces an improved version of the Fitness Dependent Optimizer, enhanced with novel initialization and dynamic weight selection techniques, to effectively solve the Economic Load Dispatch problem in power systems, reducing costs and emissions.

Contribution

The paper presents a novel variant of the Fitness Dependent Optimizer with advanced initialization and dynamic weighting, achieving better results in economic load dispatch tasks.

Findings

Enhanced optimizer reduces fuel cost, emission, and transmission loss.

Superior performance over conventional FDO in a 24-unit system.

Achieved low transmission loss of 7.94E-12.

Abstract

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.