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Agent-based solutions lend themselves well to address privacy concerns and the computational scalability needs of future distributed electric grids and end-use energy exchanges. Decentralized decision-making methods are the key to enabling peer-to-peer energy trading between electricity prosumers. However, the performance of existing decentralized decision-making algorithms highly depends on the algorithmic design and hyperparameter tunings, limiting applicability. This paper aims to address this gap by proposing a decentralized inexact projection method that does not rely on parameter tuning or central coordination to achieve the optimal solution for Peer-to-Peer (P2P) energy trading problems. The proposed algorithm does not require parameter readjustments, and once tuned, it converges for a wide range of P2P setups. Moreover, each prosumer only needs to share limited information (i.e., updated coupled variable) with neighboring prosumers. The IEEE 13 bus test system is used to showcase our proposed method's robustness and privacy advantages.

Constrained optimization problems appear in a wide variety of challenging real-world problems, where constraints often capture the physics of the underlying system. Classic methods for solving these problems rely on iterative algorithms that explore the feasible domain in the search for the best solution. These iterative methods are often the computational bottleneck in decision-making and adversely impact time-sensitive applications. Recently, neural approximators have shown promise as a replacement for the iterative solvers that can output the optimal solution in a single feed-forward providing rapid solutions to optimization problems. However, enforcing constraints through neural networks remains an open challenge. This paper develops a neural approximator that maps the inputs to an optimization problem with hard linear constraints to a feasible solution that is nearly optimal. Our proposed approach consists of four main steps: 1) reducing the original problem to optimization on a set of independent variables, 2) finding a gauge function that maps the infty-norm unit ball to the feasible set of the reduced problem, 3)learning a neural approximator that maps the optimization's inputs to an optimal point in the infty-norm unit ball, and 4) find the values of the dependent variables from the independent variable and recover the solution to the original problem. We can guarantee hard feasibility through this sequence of steps. Unlike the current learning-assisted solutions, our method is free of parameter-tuning and removes iterations altogether. We demonstrate the performance of our proposed method in quadratic programming in the context of the optimal power dispatch (critical to the resiliency of our electric grid) and a constrained non-convex optimization in the context of image registration problems.

A method and application of measuring the soot volume fraction, H2O concentration, and its temperature simultaneously in an ethylene/air premixed flame environment based on tunable diode laser abso...

An adaptive genetic algorithm with diversity-guided mutation, which combines adaptive probabilities of crossover and mutation was proposed. By means of homogeneous finite Markov chains, it is proved that adaptive genetic algorithm with diversity-guided mutation and genetic algorithm with diversity-guided mutation converge to the global optimum if they maintain the best solutions, and the convergence of adaptive genetic algorithms with adaptive probabilities of crossover and mutation was studied. The performances of the above algorithms in optimizing several unimodal and multimodal functions were compared. The results show that for multimodal functions the average convergence generation of the adaptive genetic algorithm with diversity-guided mutation is about 900 less than that of adaptive genetic algorithm with adaptive probabilities and genetic algorithm with diversity-guided mutation, and the adaptive genetic algorithm with diversity-guided mutation does not lead to premature convergence. It is also shown that the better balance between overcoming premature convergence and quickening convergence speed can be gotten.

Multi-modal function optimization must be solved when people meet with complex reality problems. With resort to the prominent search performance of CMA-ES and clustering theory, a new algorithm--K-CMA algorithm was presented. By searching local peaks and global peaks of four multi-modal functions, it can be seen from the results that this algorithm has a better result in convergence of algorithm and success rate.

Some work of calculations of probabilities of operators in genetic algorithm (GA) is summarized, and computation method of probabilities of operators is discussed. With analysis of developing situation of calculations of probabilities of operators, calculations of probabilities of operators in GA guided by cooperation of diversity and fitness is present. Analysis of dynamical curve for diversity change leads that the method of linear diversity change and the method of exponential diversity change are proposed. Modified Manhattan learning method of adaptive calculations of probabilities of operators in GA is put forward. With simulating experiments, it has been proved that the method present in the paper can improve performance of GA.

The simultaneous localization and mapping problem with evolutionary algorithms is translated to a multi-objective immune optimization problem since it inherently possesses of multi-objective characters, and in order to efficiently solve the simultaneous localization and mapping problem, a local searcher with immunity is constructed. The local searcher employs domain knowledge of the problem, which is named as a key point grid pulling that is developed in the paper. The experiment results of a real mobile robot indicate that the computational expensiveness of designed algorithms is less than other evolutionary algorithms of single-objection and multi-objective optimization problem without immunity for simultaneous localization and mapping and accuracy of obtained maps are higher.

A new intelligent control method, evolutionary control, is proposed. It was developed by integrating two of the basic regulating mechanisms of intelligent control, evolution and feedback, into one organic control paradigm. Evolutionary control brings up a new ideology for research into complex system control problems and investigation of the essence of and laws for the emergence of intelligence. It is expected that evolutionary control would be a general method to cope with the control problem of complex system.