# A modified Dung Beetle optimizer for combined heat and power economic dispatch considering power losses, valve-point effects, and operating constraints

## TL;DR

This paper introduces a modified Dung Beetle Optimizer to solve complex power system optimization problems more efficiently.

## Contribution

The novelty is proposing a modified Dung Beetle Optimizer (MDBO) with three improvement strategies for solving Combined Heat and Power Economic Dispatch problems.

## Key findings

- The MDBO significantly reduces operating costs in Combined Heat and Power Economic Dispatch problems.
- MDBO outperforms existing optimization techniques like SCSO, AVOA, and GWO in various test scenarios.
- The algorithm effectively handles power losses, valve-point effects, and prohibited operating zones.

## Abstract

The Combined Heat and Power Economic Dispatch (CHPED) problem represents a significant optimization challenge in modern power systems due to its inherent complexity arising from multiple operational constraints. This complexity is further exacerbated when considering the effect of power losses (PLs), valve-point loading effect (VPLE), and prohibited operating zones (POZs). Consequently, an efficient and robust optimization algorithm is essential for obtaining a globally optimal solution while satisfying all constraints. To address these challenges, this work evaluates the effectiveness of the Modified Dung Beetle Optimizer (MDBO) for solving the CHPED problem, considering PLs, VPLE, and POZs. The MDBO enhances the search process and mitigates the limitations of the conventional Dung Beetle Optimizer, particularly stagnation and premature convergence to local optima. The novelty in this paper is proposing a modified version of the traditional DBO (MDBO) by integrating three improvement strategies, including the fitness distance balance (FDB), Chaotic mutation (CM), and adaptive local search approach (ALSA), to solve the CHPED problem. The proposed MDBO has the ability to overcome the shortcomings of traditional DBO, such as its premature convergence and tendency to local optima. The effectiveness of the proposed MDBO has been evaluated on CHPED problems involving 4-unit, 7-unit, 24-unit, and 48-unit systems under various operating conditions. Moreover, MDBO performance has been rigorously assessed using standard benchmark test suites, including CEC-2019. The results demonstrate a significant reduction in operating costs, confirming the superior performance of the MDBO in comparison to existing optimization techniques like Sand Cat Swarm Optimizer (SCSO), African vultures optimization algorithm (AVOA), Sine Cosine Algorithm (SCA), Harris Hawks Optimization (HHO), Grey Wolf Optimizer (GWO), Liver cancer algorithm (LCA), Zebra Optimizer Algorithm (ZOA), and Whale Optimization Algorithm (WOA). Furthermore, the proposed algorithm consistently outperforms alternative methods reported in the literature, offering a more efficient and reliable solution for CHPED optimization.

The online version contains supplementary material available at 10.1038/s41598-025-32060-4.