A novel adaptive parameter strategy differential evolution algorithm and its application in midcourse guidance maneuver decision-making

A novel trajectory optimization technique has been proposed in this paper to generate reference trajectory for Satellite Launch Vehicle (SLV). For this purpose, a suitable objective function has been formulated subsuming parameters like height, velocity, injection angle and payload mass. The contribution of these parameters in evolving the objective function has been represented by assigning different weighting factors against the different parameters. Moreover, optimal selection of the setting of parameters has also been achieved by proper optimization of the weighting factors associated with the objective function. In the next step, an attempt has been made to optimize the objective function using the existing Differential Evolution (DE) scheme. As an enhancement of this work, a novel Self Adaptive Differential Evolution (SADE) algorithm has been proposed to optimize the trajectory in a more effective manner. Finally, a comparative study has been presented between the proposed SADE based design and the existing DE based design.

JADE is an state-of-the-art adaptive differential evolution algorithm which implements “DE/current-to-pbest” as its mutation strategy, adapts its mutation factor and crossover rate, and uses an optional external archive to keep track of potential removed individuals in previous generations. This paper proposes MPADE, which extends JADE by using a multi-populated approach to solve high dimensional real-parameter optimization problems. This mechanism helps preventing the two well-known problems affecting the differential evolution algorithm performance, which are premature convergence and stagnation. The algorithm was tested using the benchmark functions in IEEE Congress on Evolutionary Computation 2014 test suite. MPADE was compared using Wilcoxon test to JADE algorithm and with other state-of-the-art algorithms that either use a multi-population approach or adapt their parameters. The experimental results show that the proposed new algorithm improves significantly its precursor and it is also suggested that other state-of-the-art algorithms could benefit from the multi-populated based approach.

Multi-objective robust dynamic pricing and operation strategy optimization for integrated energy system based on stackelberg game

An effective coordination in an interconnected system can be obtained by ensuring proper settings of overcurrent relays. In this research work, the problem of coordinating the overcurrent relays is formulated as a non‐linear problem, taking into account the as discrete nature of time dial settings and the pickup current settings. An effective approach based on adaptive differential evolution (ADE) algorithm is proposed to solve the coordination problem. The results obtained by ADE are compared with different algorithms on different model test systems and found to be feasible, robust and efficient.

In this paper, a new adaptive grouping differential evolution (AGDE) algorithm is proposed to improve the optimization performance by implementing a prediction strategy of the constraints for constrained optimization problems. It is unnecessary to calculate the constraint values when dealing with the constraints in this method. The constraints are handled after a simple prediction according to the Lipschitz condition. When the constraints are very complex, the load arisen from the calculation of the constraint values is reduced dramatically and the feasibility of the solutions remains with great probability. In AGDE algorithm, the population is dynamically grouped to three subpopulations with respective newly-designed mutation strategy. Meanwhile, the mutation factor and crossover probability are adopted associated with the evolutionary process according to the information of the entire population. Both of the above improvements not only increase the diversity of population and speed up the convergence, but also reduce the complexity of the parameter selection. Four sets of comparative experiments are carried out to evaluate the feasibility and effectiveness of the proposed method that deals with the constraints.

BACKGROUND: Normative performance (NP) describes the pilots adherence to tactics, techniques, and procedures (TTPs). Until now, there has not been a global NP measurement technique for beyond visual range (BVR) air combat, and the methodology and technology related to the evaluation of NP have fallen behind the pace of the overall technical progress of distributed mission operations (DMO) training.METHODS: Platform-independent core air combat tasks were identified. The execution of these tasks is directed with TTPs. BVR air combat missions were flown in a DMO simulator system and the design NP was varied between missions. Observers viewed debriefs of these missions and attempted to identify TTP-regulated air combat tasks. Once identified, they scored the pilots NP in those tasks. The scoring was based on the level of TTP adherence and the impact a nonadherence had on the mission accomplishment.RESULTS: All observers were able to identify most of the TTP-regulated air combat tasks. There was a strong positive correlation between the observed and design NP scores. The overall Kappa indicated a fair agreement between the observers. The percentage of observers NP assessments which agreed with the design NP varied from 49.60 to 85.28% in different air combat missions. On average, 73.9%6 of the observers NP scores agreed with the design NP scores.CONCLUSIONS: Observers were able to accurately identify TTP-regulated tasks and score NP of these tasks during an air combat debrief. There was a moderate agreement between the observers NP scores.Mansikka H, Virtanen K, Mkinen L, Harris D. Normative performance measurement in simulated air combat. Aerosp Med Hum Perform. 2021; 92(11):908-912.

This work aims to provide an engagement decision support tool for Beyond Visual Range (BVR) air combat in the context of Defensive Counter Air (DCA) missions. In BVR air combat, engagement decision refers to the choice of the moment the pilot engages a target by assuming an offensive stance and executing corresponding maneuvers. To model this decision, we use the Brazilian Air Force's Aerospace Simulation Environment (Ambiente de Simulação Aeroespacial - ASA in Portuguese), which generated 3,729 constructive simulations lasting 12 minutes each and a total of 10,316 engagements. We analyzed all samples by an operational metric called the DCA index, which represents, based on the experience of subject matter experts, the degree of success in this type of mission. This metric considers the distances of the aircraft of the same team and the opposite team, the point of Combat Air Patrol, and the number of missiles used. By defining the engagement status right before it starts and the average of the DCA index throughout the engagement, we create a supervised learning model to determine the quality of a new engagement. An algorithm based on decision trees, working with the XGBoost library, provides a regression model to predict the DCA index with a coefficient of determination close to 0.8 and a Root Mean Square Error of 0.05 that can furnish parameters to the BVR pilot to decide whether or not to engage. Thus, using data obtained through simulations, this work contributes by building a decision support system based on machine learning for BVR air combat.

Differential evolution (DE), as an extremely powerful evolutionary algorithm, has recently been widely employed within complex reality optimisation problems. However, the DE algorithm mainly focuses on strengthening the adaptability of exploitation, which allows the sensitivity of the DE algorithm to be solved in cases where the effects of solving various types of problems are quite different. Moreover, the local search ability and population diversity have not been solved properly. Therefore, an adaptive DE algorithm using fitness distance correlation and a neighbourhood-based strategy (FNADE) is proposed. FNADE introduces the fitness distance correlation (FDC) as the basis for judging the difficulty of the problem, utilises a Voronoi diagram to increase the population diversity for complex multimodal problems and adopts the neighbourhood-based mutation strategy to strengthen the local search capability. FNADE is committed to solving unconstrained single-objective optimisation problems. The proposed algorithm is compared with six advanced DE algorithms in terms of CEC2017 benchmark functions. The experimental results show that the adaptive DE algorithm using FNADE is superior to other DE algorithms with regard to the accuracy and population diversity of the solution.

Multi-agent cooperative target tracking plays a significant role in distributed artificial intelligence for unmanned aerial vehicle (UAV) systems. Most existing tracking solutions using online path planning focus on general target trajectories. Such trajectories have strong regularity and are smooth with minimal fluctuations. However, in real UAV applications a target will change its trajectory frequently to prevent from being tracked, and thus its trajectory will fluctuate greatly and randomly. To this end, in this paper we consider a realistic scenario where the UAV target trajectory is changeable and random. For real-time target tracking, we formulate the tracking task as a distributed model predictive control (DMPC) problem, the objective of which is to optimize tracking performance under various constraints. To do so, we innovatively combine the adaptive differential evolution (ADE) algorithm with Nash optimization, and then propose a Nash-combined ADE method. Specifically, we use ADE to adaptively adjust the predicted trajectory of each UAV agent and Nash optimization to efficiently solve DMPC formulation for global optimization, which can improve the tracking accuracy and simultaneously reduce the computational complexity. The simulation results show that the proposed method performs well in terms of tracking accuracy, UAV collision avoidance and tracking stability for realistic target trajectories.

Design of non-uniform linear antenna arrays is one of the most important electromagnetic optimization problems of current interest. In this article, an adaptive Differential Evolution (DE) algorithm has been used to optimize the spacing between the elements of the linear array to produce a radiation pattern with minimum side lobe level and null placement control. DE is arguably one of the best real parameter optimizers of current interest takes very few control parameters and is easy to implement in any programming language. In this study two very simple adaptation schemes are used to regulate the control parameters F and Cr, upon which the performance of DE is critically dependent. The adaptation schemes are based on the objective function values of the target vectors and donor vectors. The adaptive DE-variant has been used to solve three difficult instances of the design problem and the optimization goal in each example is easily achieved. The results of the proposed algorithm have been shown to meet or beat the recently published results obtained using other state-of-the-art metaheuristics like the Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Memetic Algorithms (MA), and Tabu Search (TS) in a statistically meaningful way.

Parallel robot inverse kinematics solution is relatively easier to obtain than forward kinematics forward, this paper assumes that the forward kinematics of six-DOF parallel robot is given then using the kinematics inverse solution to approximate the forward solution. To improve efficiency and stability of the approximation, an adaptive parameters differential evolution algorithm is proposed and applied to the forward kinematics problem. The experimental results show that the modified algorithm can gain the forward kinematics of parallel robot efficiently.

This paper proposes an evolutionary computing based automatic partitioned clustering of probability density function, the so-called binary adaptive elitist differential evolution for clustering of probability density functions (baeDE-CDFs). Herein, the k-medoids based representative probability density functions (PDFs) are preferred to the k-means one for their capability of avoiding outlier effectively. Moreover, addressing clustering problem in favor of an evolutionary optimization one permits determining number of clusters “on the run”. Notably, the application of adaptive elitist differential evolution (aeDE) algorithm with binary chromosome representation not only decreases the computational burden remarkably, but also increases the quality of solution significantly. Multiple numerical examples are designed and examined to verify the proposed algorithm’s performance, and the numerical results are evaluated using numerous criteria to give a comprehensive conclusion. After some comparisons with other algorithms in the literature, it is worth noticing that the proposed algorithm reveals an outstanding performance in both quality of solution and computational time in a statistically significant way.

Adaptive constraint differential evolution for optimal power flow

Q-Learning-based parameter control in differential evolution for structural optimization

In this paper an adaptive differential evolution algorithm with dynamic changes of population size is presented. In proposed algorithm an adaptive selection of control parameters of the algorithm are introduced. Due to these parameters selection, the algorithm gives better results than differential evolution algorithm without this modification. Also, in presented algorithm dynamic changes of population size are introduced. This modification try to overcome limitations connected with premature convergence of the algorithm. Due to dynamic changes of population size, the algorithm can easier get out from local minimum. The proposed algorithm is used to train artificial neural networks. Results obtained are compared with those obtained using: adaptive differential evolution algorithm without dynamic changes of population size, method based on evolutionary algorithm, error back-propagation algorithm, and Levenberg-Marquardt algorithm.