Article Details

Abstract

To address the problems of weak ability to escape from local optima and low solution accuracy existing in current algorithms when solving the Economic Dispatch Problem, this paper proposes a multi-mechanism Dream Optimization Algorithm. This algorithm enhances global exploration ability by introducing a differential mutation mechanism, improves local exploitation accuracy by adopting a Gaussian mutation mechanism, combines an improved memory forgetting and supplementation mechanism to further promote optimization performance, and realizes a dynamic balance between global and local search abilities by aid of a roulette wheel selection mechanism. The experimental results based on a 40-unit economic dispatch model indicate that compared with traditional optimization algorithms and other meta-heuristic algorithms, this algorithm shows significant improvement in escaping from local optima and convergence accuracy of the solution. The fuel cost of the obtained optimal solution is reduced by at least 2.16%, which verifies its effectiveness and engineering application value in the Economic Dispatch Problem.

Keywords

Economic Dispatch Problem; Dream Optimization Algorithm; Gaussian mutation; differential mutation; roulette wheel selection

References

1. [1] SAID M，HOUSSEIN E H，ALDAKHEEL E A，et al. Performance of the Walrus Optimizer for Solving an Economic Load Dispatch Problem [J].AIMS Mathematics，2024，9（4）：10095-10120.
2. [2] PATTY S，DAS R，MANDAL D，et al. Self-adaptive Multi-population Quadratic Approximation Guided Jaya Optimization Applied to Economic Load Dispatch Problems With or Without Valve-point Effects [J].Results in Control and Optimization，2025，19：100543.
3. [3] KAUR A，SINGH M，DHILLON J S. Oppositionally Driven Crisscross Gravitational Search Approach for Economic Load Dispatch [J].Electrical Engineering，2025：1-39.
4. [4] ALAWAD N A，ABED-ALGUNI B H，EL-IBINI M. Hybrid Snake Optimizer Algorithm for Solving Economic Load Dispatch Problem With Valve Point Effect [J].The Journal of Supercomputing，2024，80（13）：19274-19323.
5. [5] LANG Y F，GAO Y L. Dream Optimization Algorithm (DOA): A Novel Metaheuristic Optimization Algorithm Inspired by Human Dreams and Its Applications to Real-world Engineering Problems [J].Computer Methods in Applied Mechanics and Engineering，2025，436：117718.
6. [6] BRAIK M S，AWADALLAH M A，AL-BETAR M A，et al. A Non-Convex Economic Load Dispatch Problem Using Chameleon Swarm Algorithm With Roulette Wheel and Levy Flight Methods [J].Applied Intelligence，2023，53（14）：17508-17547.
7. [7] DAS S，SUGANTHAN P N. Problem Definitions and Evaluation Criteria for CEC 2011 Competition on Testing Evolutionary Algorithms on Real World Optimization Problems [R].Kolkata：Jadavpur University, Nanyang Technological University，2010：41-359.
8. [8] LUKŠAN L，MATONOHA C，VLČEK J. Interior Point Methods for Large-scale Nonlinear Programming [J].Optimization Methods and Software，2005，20（4-5）：569-582.
9. [9] GILL P E，MURRAY W，SAUNDERS M A. SNOPT: An SQP Algorithm for Large-scale Constrained Optimization [J].SIAM Review，2005，47（1）：99-131.
10. [10] MIRJALILI S. Moth-flame Optimization Algorithm: A Novel Nature-inspired Heuristic Paradigm [J].Knowledge-Based Systems，2015，89：228-249.
11. [11] SU H，ZHAO D，HEIDARI A A，et al. RIME: A Physics-based Optimization [J].Neurocomputing，2023，532：183-214.
12. [12] ABDOLLAHZADEH B，GHAREHCHOPOGH F S，KHODADADI N，et al. Mountain Gazelle Optimizer: A New Nature-inspired Metaheuristic Algorithm for Global Optimization Problems [J].Advances in Engineering Software，2022，174：103282.

Funding

Research Foundation Ability Enhancement Project for Young and Middle aged Teachers in Guangxi Universities (2023KY1323)

About the authors

Meng Tuanxing (1995-), male, Zhuang nationality, from Nanning, Guangxi, lecturer, master's degree, research direction: optimization theory, machine learning.