Researchers have developed an improved optimization algorithm designed to solve the optimal power flow problem, a central challenge in operating electrical power systems. The method, called EMASDCS, builds on the differentiated creative search algorithm by adding two cooperating mechanisms: an echo localization search strategy and an adaptive memory enhanced learning strategy. The work was published in Cluster Computing.
Optimal power flow seeks to optimize specific objective functions by adjusting control variables while respecting operational constraints and power balance equations.
The echo localization search strategy integrates information from the iterative process, the stagnation state and historical experience to dynamically adjust the search radius. When the algorithm detects that progress has stalled, it automatically activates an exploration reinforcement mechanism, helping the search escape unproductive regions of the solution space.
The adaptive memory enhanced learning strategy works differently. It builds dimensional cognitive systems and elite memory banks, and drives variational operators to fuse random exploration, memory guidance and creative perturbation. According to the authors, this combination is designed to achieve accurate search for key dimensions of the problem.
The two strategies are linked in a feedback loop. The dynamically adjusted search radius produced by the echo localization strategy provides strategic guidance for the memory enhanced learning component, while the cognitive feedback from that component in turn optimizes the stagnation judgment of the echo localization strategy. The authors describe this arrangement as a two-way intelligent decision-making closed loop.
To assess optimization performance, the team evaluated the proposed algorithm on the CEC 2022 benchmark functions against nine competitors: DCS, ESSDCS, AMDCS, ALO, ECO, JHO, LGC, NOA and PLO. For fairness, all comparative tests adopted identical total function evaluations, and average CPU time was recorded to evaluate computational efficiency.
The algorithm was then applied to the optimal power flow problem through simulations on the IEEE 30-bus and IEEE 118-bus test systems, standard benchmarks in power systems research. Four optimization objectives were selected: generation cost, active power loss, voltage stability margin and bus voltage deviation.
The reported results indicate that the proposed algorithm achieves competitive computational efficiency while effectively addressing complex optimal power flow problems.
The study is available in Cluster Computing, volume 29, article number 911, published on 28 October 2026. As with all simulation-based algorithm studies, the findings concern the tested benchmark functions and IEEE test systems rather than operational grid deployments.
Subject of Research: A hybrid metaheuristic algorithm for solving the optimal power flow problem in power systems
Article Title: Differentiated creative search algorithm based on adaptive memory enhanced learning and echo localization search strategy for solving optimal power flow problem in power system
Article References: Zhao, J.-H., Wang, J.-S., Lu, J.-Y., Yan, B., Zhang, S.-B., & Fu, J.-Y. (2026). Differentiated creative search algorithm based on adaptive memory enhanced learning and echo localization search strategy for solving optimal power flow problem in power system. Cluster Computing, 29(16), Article 911. https://doi.org/10.1007/s10586-026-06698-2
Image Credits: AI Generated
DOI: 10.1007/s10586-026-06698-2
Keywords: optimal power flow, metaheuristic algorithm, differentiated creative search, echo localization search, adaptive memory enhanced learning, IEEE 30-bus, IEEE 118-bus, generation cost, active power loss, voltage stability, CEC 2022 benchmarks, Cluster Computing
News Source: Denise Maddox. (October 29, 2026). New Hybrid Algorithm Tackles Optimal Power Flow in Electric Grids. Scienmag.
