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One key component in a jet engine test cell is the blast basket, which mixes the jet flow exiting the nozzle with ambient air to aid in cooling, reducing velocity, and decreasing noise. The test cell uses the ejector pump method. Critical blast basket design parameters include diameter, length, and perforation pattern. In this study, the basket length is predefined, diameter is calculated from the augmentor design, and the hole count is based on material porosity. To evaluate mass flow uniformity, five 2-meter-wide strips along the basket are modeled, aiming for uniform distribution across these strips. A major design challenge is flow concentration at the downstream end, so this study optimizes the hole configuration and cone angle at the basket’s end to improve distribution. The single-objective Differential Evolution method is employed, using hole diameter, material porosity, and outlet cone half-angle as design variables, with the objective function defined as minimizing the standard deviation of mass flow across the strips. The flow field was computed using ANSYS Fluent 2020 R1 with the k–ω Shear Stress Transport  (SST) turbulence model, while geometry generation under design constraints is automated using a custom C code. Results show an 18.1% reduction in mass flow standard deviation compared to the baseline, indicating improved uniformity. Additionally, acoustic intensity at the strip exits decreases significantly, with reductions of 5.81%, 15.35%, 31.71%, and 55.13% across the first to fourth strips in the optimized configuration.

Load flow analysis is an essential tool for the reliable planning and operation of interconnected power systems. The constant increase in power demand, apart from the increased intermittency in power generation due to renewable energy sources without proportionate augmentation in transmission system infrastructure, has driven the power systems to function nearer to their limits. Though the power flow (PF) solution may exist in such circumstances, the traditional Newton–Raphson based PF techniques may fail due to computational difficulties owing to the singularity of the Jacobian Matrix during critical conditions and faces difficulties in solving ill-conditioned systems. To address these problems and to assess the impact of large-scale photovoltaic generator (PVG) integration in power systems on power flow studies, a derivative-free quasi-oppositional heap-based optimization (HBO) (QOHBO) technique is proposed in the present paper. In the proposed approach, the concept of quasi-oppositional learning is applied to HBO to enhance the convergence speed. The efficacy and effectiveness of the proposed QOHBO-PF technique are verified by applying it to the standard IEEE and ill-conditioned systems. The robustness of the algorithm is validated under the maximum loadability limits and high R/X ratios, comparing the results with other well-known methods suggested in the literature. The results thus obtained show that the proposed QOHBO-PF technique has less computation time, further enhancement of reliability in the presence of PVG, and has the ability to provide multiple PF solutions that can be utilized for voltage stability analysis.

The multi-robot path planning problem is an NP-hard problem. The coati optimization algorithm (COA) is a novel metaheuristic algorithm and has been successfully applied in many fields. To solve multi-robot path planning optimization problems, we embed two differential evolution (DE) strategies into COA, a self-adaptive differential evolution-based coati optimization algorithm (SDECOA) is proposed. Among these strategies, the proposed algorithm adaptively selects more suitable strategies for different problems, effectively balancing global and local search capabilities. To validate the algorithm’s effectiveness, we tested it on CEC2020 benchmark functions and 48 CEC2020 real-world constrained optimization problems. In the latter’s experiments, the algorithm proposed in this paper achieved the best overall results compared to the top five algorithms that won in the CEC2020 competition. Finally, we applied SDECOA to optimization multi-robot online path planning problem. Facing extreme environments with multiple static and dynamic obstacles of varying sizes, the SDECOA algorithm consistently outperformed some classical and state-of-the-art algorithms. Compared to DE and COA, the proposed algorithm achieved an average improvement of 46% and 50%, respectively. Through extensive experimental testing, it was confirmed that our proposed algorithm is highly competitive. The source code of the algorithm is accessible at: https://ww2.mathworks.cn/matlabcentral/fileexchange/164876-HDECOA.

Full‐wave electromagnetic (EM) simulation, particularly in environments such as CST Studio Suite, makes large‐scale antenna optimization computationally prohibitive. We introduce an adaptive surrogate‐assisted differential evolution (DE) framework, implemented via a unified CST–Python workflow, designed to accelerate design‐on‐demand antenna optimization. The workflow integrates target‐frequency‐driven design requests, multimetric antenna performance targeting, cross‐validated surrogate selection, selective CST validation, and iterative CST‐verified dataset updating. It begins with Latin hypercube sampling (LHS) to create a CST‐simulated training set, selects a regressor (KNN, RF, SVR, GB, XGBoost) via five‐fold cross‐validation based on mean squared error, and then uses the surrogate to guide the DE search. The core adaptive mechanism involves mandatory full‐wave validation of the best design candidate from each optimization cycle, appending the verified result to the training dataset to enable targeted model refinement. Optimization is governed by a multiobjective penalized aggregate function that minimizes the resonant‐frequency error while maximizing the design performance metrics of bandwidth, return loss, and gain. We evaluated this approach on three antenna families—dipole (2.00 GHz), microstrip patch (2.55 GHz), and Yagi–Uda (2.50 GHz)—and met targets with only 10–12 full‐wave validations per run. Our method achieved a verified design in 9–16 min, whereas pure DE took 21–113 min with 28–55 full‐wave solves, and pure PSO took 18–217 min with 28–106 full‐wave solves. This corresponds to speedups of 2.38–8.06× and 2.04–13.68×, respectively. This work demonstrates that integrating an adaptively selected surrogate model into the optimization strategy substantially reduces the computational cost of full‐wave analysis, establishing a highly efficient and robust methodology for diverse EM design applications. An adaptive surrogate‐assisted differential evolution framework integrates machine learning‐based surrogate modeling with selective full‐wave electromagnetic validation to accelerate antenna optimization. A CST–Python workflow combines Latin‐hypercube sampling, cross‐validated model selection, and iterative dataset refinement to guide the search toward optimal designs. The approach significantly reduces computational cost by minimizing expensive simulations while maintaining high accuracy, achieving faster convergence and reliable performance across multiple antenna topologies.

This paper proposes a novel distributed differential evolution framework called distributed mixed variants (dynamic) differential evolution ( d m v D 2 E ) . This novel framework is a heterogeneous mix of effective differential evolution ( DE ) and dynamic differential evolution ( DDE ) variants with diverse characteristics in a distributed framework to result in d m v D 2 E . The d m v D 2 E , discussed in this paper, constitute various proportions and combinations of DE/best/2/bin and DDE/best/2/bin as subpopulations with each variant evolving independently but also exchanging information amongst others to co-operatively enhance the efficacy of d m v D 2 E as whole. The d m v D 2 E variants have been run on 14 test problems of 30 dimensions to display their competitive performance over the distributed classical and dynamic versions of the constituent variants. The d m v D 2 E , when benchmarked on a different 13 test problems of 500 as well as 1,000 dimensions, scaled well and outperformed, on an average, five existing distributed differential evolution algorithms.

Operating a reservoir during flooding is a complex problem in which optimum decision-making is a difficult task. The present study demonstrates a solution for the operation of flooding problem in a multiple-purpose reservoir. A reservoir on River Narmada in central India is chosen as the case study. The multiple objective problems comprised maximization of hydropower releases, minimizing spills, and achieving stipulated target storage at the end of the operation period. The chosen optimization models are the Differential Evaluation Algorithm (DEA) and its variants: the Enhanced Differential Evolution Algorithm (EDEA) and the Modified Enhanced Differential Evaluation Algorithm (MEDEA). The EDEA model is modified in the present study to MEDEA. The results of all three models applied to the same case study are compared on convergence to an optimal solution. All three algorithms were tested on two of the popular benchmark functions that are Ackley and Sphere. The results of both applications demonstrated that MEDEA proved to be the best in terms of converging to the optimal solution, exhibiting better stability, and quality of final results. The outcomes of this study also provided an effective way to optimize large scale multi-purpose and multi-reservoir flood control operation problems.

Water quality management and control has high importance in planning and developing of water resources. This study investigated application of Genetic Algorithm (GA), Ant Colony Optimization for Continuous Domains (ACO R ) and Differential Evolution (DE) in improving the performance of adaptive neuro-fuzzy inference system (ANFIS), for evaluating the quality parameters of Gorganroud River water, such as Electrical Conductivity (EC), Sodium Absorption Ratio (SAR) and Total Hardness (TH). Accordingly, initially most suitable inputs were estimated for every model using sensitivity analysis and then all of the quality parameters were predicted using mentioned models. Investigations showed that for predicting EC and TH in test stage, ANFIS-DE with R 2 values of 0.98 and 0.97, respectively and RMSE values of 73.03 and 49.55 and also MAPE values of 5.16 and 9.55, respectively were the most appropriate models. Also, ANFIS-DE and ANFIS-GA models had the best performance in prediction of SAR (R 2 = 0.95, 0.91; RMSE = 0.43, 0.37 and MAPE = 13.43, 13.72) in test stage. It is noteworthy that ANFIS showed the best performance in prediction of all mentioned water quality parameters in training stage. The results indicated the ability of mentioned algorithms in improving the accuracy of ANFIS for predicting the quality parameters of river water.

A multi-reservoir system’s operation during floods is a complex problem, because it is dynamic and nonlinear nature, finding the global or near-global best solution is a difficult task. The Adaptive Immune Differential Evolution (AIDE) algorithm is one of the Evolutionary Algorithms (EA) used to solve the multi-reservoir system. It improves the Differential Evolution (DE) algorithm’s exploitation and exploration capabilities. A multi-criteria decision-making (MCDM) approach is also implemented for managing flood control operations in reservoirs, aiming to handle correlations among different criteria. To eliminate correlation, principal component Analysis (PCA) is used and coupled with a weight vector as the input to the TOPSIS method, WASPAS method, and MOORA method by which the alternatives are to be determined. The results show that the dimensionality of the criterion system is lowered while simultaneously eliminating the correlation between criteria, and the ranking order of the alternatives is fair. From the results, it is clear that the AIDE algorithm would have faster convergence and a powerful global ability than the DE algorithm. The control parameters used in DE and AIDE enable these algorithms to effectively navigate complex search spaces and identify optimal or near-optimal solutions. The above optimization method is recommended for complex, large-scale reservoir operations and evaluation of ranking by MCDM approach is helpful for flood control operation of multi-reservoir systems. However, the optimal alternative sequence (3, 5, 2, 4, and 1) is suitable to manage the flood event operation by multi-reservoir.

Differential evolution (DE) stands out as a straightforward yet remarkably powerful evolutionary algorithm employed for real-world problem-solving purposes. In the DE algorithm, few parameters are used, and the population is evolved by applying various operations. It is difficult in evolutionary computation algorithms to maintain population diversity. The main issue is the sub-population of the DE algorithm that helps improve convergence speed and escape from the local optimum. Evolving sub-populations by maintaining diversity is an important issue in the literature that is considered in this research. A solution is proposed that uses sub-populations to promote greater diversity within the population and improve the algorithm performance. DE, heterogeneous distributed differential evolution (HDDE), multi-population ensemble differential evolution (MPEDE), and the proposed improved multi-population ensemble differential evolution (IMPEDE) are implemented using parameter settings; population sizes of 100 NP, 150 NP, and 200 NP; and dimensions of 10D, 30D, and 50D for performance comparison. Different combinations of mutations are used to generate the simulated results. The simulation results are generated using 1000, 3000, and 5000 iterations. Experimental outcomes show the superior results of the proposed IMPEDE over existing algorithms. The non-parametric significance Friedman test confirms that there is a significant difference in the performance of the proposed algorithm and other algorithms used in this study by considering a 0.05 level of significance using six benchmark functions.

Dynamic environmental economic dispatch (DEED) with wind power is an important extension of the classical environmental economic dispatch (EED) problem, which could provide reasonable scheduling scheme to minimize the pollution emission and economic cost at the same time. In this study, the combined dynamic scheduling of thermal power and wind power is carried out with pollutant emission and economic cost as optimization objectives; meanwhile, the valve‐point effect, power balance, ramp rate, and other constraints are taken into consideration. In order to solve the DEED problem, an enhanced multi‐objective differential evolution algorithm (EMODE) is proposed, which adopts the superiority of feasible solution (SF) and nondominated sorting (NDS) two selection strategies to improve the optimization effect. The suggested algorithm combines the total constraint violation and penalty function to deal with various constraints, due to different constraint techniques could be effective during different stages of searching process, and this method could ensure that each individual in the Pareto front (PF) is feasible. The results show that the proposed algorithm can deal with DEED problem with wind power effectively, and provide better dynamic scheduling scheme for power system. For the problem of power system dynamic dispatch with wind power, an enhanced multi‐objective differential evolution algorithm is proposed in this paper, which adopts two selection strategies and different constraint handling process techniques. The suggested method has strong ability for the problem of dispatch, and from the simulation results, we can obtain that this method could provide better dispatch scheme for decision‐makers.