Development of a method of structural-parametric assessment of the object state

A method of structural and parametric assessment of the object state has been developed. The essence of the method is to provide an analysis of the current state of the object under analysis. The key difference of the developed method is the use of advanced procedures for processing undefined initial data, selection, crossover, mutation, formation of the initial population, advanced procedure for training artificial neural networks and rounding coordinates. The use of the method of structural-parametric assessment of the object state allows increasing the efficiency of object state assessment. An objective and complete analysis is achieved using an advanced algorithm of evolution strategies. The essence of the training procedure is the training of synaptic weights of the artificial neural network, the type and parameters of the membership function, the architecture of individual elements and the architecture of the artificial neural network as a whole. An example of using the proposed method in assessing the operational situation of the troops (forces) grouping is given. The developed method is 30–35 % more efficient in terms of the fitness of the obtained solution compared to the conventional algorithm of evolution strategies. Also, the proposed method is 20–25 % better than the modified algorithms of evolution strategies due to the use of additional improved procedures according to the criterion of fitness of the obtained solution. The proposed method can be used in decision support systems of automated control systems (artillery units, special-purpose geographic information systems). It can also be used in DSS for aviation and air defense ACS, DSS for logistics ACS of the Armed Forces of Ukraine

Deep Reinforcement Learning Versus Evolution Strategies: A Comparative Survey

<div>Deep Reinforcement Learning (DRL) and Evolution Strategies (ESs) have surpassed human-level control in many sequential decision-making problems, yet many open challenges still exist.</div><div>To get insights into the strengths and weaknesses of DRL versus ESs, an analysis of their respective capabilities and limitations is provided. </div><div>After presenting their fundamental concepts and algorithms, a comparison is provided on key aspects such as scalability, exploration, adaptation to dynamic environments, and multi-agent learning. </div><div>Then, the benefits of hybrid algorithms that combine concepts from DRL and ESs are highlighted. </div><div>Finally, to have an indication about how they compare in real-world applications, a survey of the literature for the set of applications they support is provided.</div>

A Simplified, Efficient Approach to Hybrid Wind and Solar Plant Site Optimization

Wind plant layout optimization is a difficult, complex problem with a large number of variables and many local minima. Layout optimization only becomes more difficult with the addition of solar generation. In this paper, we propose a parameterized approach to wind and solar hybrid power plant layout optimization that greatly reduces problem dimensionality while guaranteeing that the generated layouts have a desirable regular structure. We argue that the evolution strategies class of derivative-free optimization methods is well-suited to the parameterized hybrid layout problem, and we demonstrate how hard layout constraints (e.g. placement restrictions) can be transformed into soft constraints that are amenable to optimization using evolution strategies. Next, we present experimental results on four test sites, demonstrating the viability, reliability, and effectiveness of the parameterized ES approach for generating optimized hybrid plant layouts. Completing the tool kit for parameterized ES layout generation, we include a brief tutorial describing how the parameterized ES approach can be inspected, understood, and debugged when applied to hybrid plant layouts.

Convergence Analysis of the Hessian Estimation Evolution Strategy

The class of algorithms called Hessian Estimation Evolution Strategies (HE-ESs) update the covariance matrix of their sampling distribution by directly estimating the curvature of the objective function. The approach is practically efficient, as attested by respectable performance on the BBOB testbed, even on rather irregular functions. In this paper we formally prove two strong guarantees for the (1+4)-HE-ES, a minimal elitist member of the family: stability of the covariance matrix update, and as a consequence, linear convergence on all convex quadratic problems at a rate that is independent of the problem instance.