In this study, we propose a predefined-time multiagent approach for multiobjective optimization. Predefined-time optimization is an that can converge to state extremely close optimal solution at given time. A time-base generator derived and applied the approaches achieving The problem reformulated as distributed and, thus, solved in private safe manner. For optimization, system with generators developed its convergence speed are proven. Several examples confirm validity of results.

This article studies the constrained optimization problems in quaternion regime via a distributed fashion. We begin with presenting some differences for generalized gradient between real and domains. Then, an algorithm considered problem is given, by which desired transformed into unconstrained setup. Using tools from Lyapunov-based technique nonsmooth analysis, convergence property associated devised further guaranteed. In addition, designed has potential solving neurodynamic as recurrent...

In this paper, we address the Clifford-valued distributed optimization subject to linear equality and inequality constraints. The objective function of problems is composed sum convex functions defined in Clifford domain. Based on generalized gradient, a system multiple recurrent neural networks (RNNs) proposed for solving problems. Each RNN minimizes local individually, with interactions others. convergence rigorously proved based Lyapunov theory. Two illustrative examples are delineated...

In this article, two types of multiagent systems (MASs) are developed for distributed bilevel constrained optimization. Within the framework optimization modeling, objective function is in a summation manner local functions. Multiple agents connected via communication network harnessed optimizing functions cooperatively while adhering to coupled constraints with global information, and each agent tasked solving an individual inner problem it subject multiple constraints. To address...

In this article, we present a collaborative neurodynamic approach to distributed optimization with nonconvex functions. We develop recurrent neural network (RNN) group by connecting individual projection networks through communication network. prove the convergence of RNN local optimal solutions given problem. propose system multiple groups for scattered searches and metaheuristic rule reinitializing neuronal states upon their convergence. elaborate on three numerical examples demonstrate...

This technical note proposes a decentralized-partial-consensus optimization (DPCO) problem with inequality constraints. The partial-consensus matrix originating from the Laplacian is constructed to tackle A continuous-time algorithm based on multiple interconnected recurrent neural networks (RNNs) derived solve problem. In addition, nonsmooth analysis and Lyapunov theory, convergence of further proved. Finally, several examples demonstrate effectiveness main results.

In this paper, we propose a continuous-time multi-agent system via event-triggered communication among agents for distributed optimization. We develop dynamic bi-event triggering rule based on both local decision variables and auxiliary to reduce costs. design triggered the Karush-Kuhn-Tucker conditions, which allows initializing arbitrarily hence relaxing existing zero-sum condition initial values of variables. prove exponential convergence optimal solution derive lower bound rate....

In this article, we propose a collaborative neurodynamic optimization (CNO) method for the distributed seeking of generalized Nash equilibriums (GNEs) in multicluster games with nonconvex functions. Based on an augmented Lagrangian function, develop projection neural network local search GNEs, and its convergence to GNE is proven. We formulate global problem which optimal solution high-quality GNE, adopt CNO approach consisting multiple recurrent networks scattering searches metaheuristic...

In this paper, a class of penalty-function-type multi-agent approaches via communication networks is developed for distributed nonconvex optimal resource allocation. A method utilized to handle networked allocation constraints, and employed handling global information in manner. Then, system constructed model, its stability with local minimizer proven. Further, model subject "on/off" constraints introduced. Based on an augmented Lagrangian function, another developed, it proven be stable...

This article focuses on a distributed optimization problem subject to partial-impact cost functions that relates two decision variable vectors. To this end, algorithms are presented with the aim of solving considered in structure fashion and gradient fashion, respectively. Furthermore, connection between equilibrium induced algorithm involved is established, aid tools from nonsmooth analysis change coordinate theorem. Two numerical examples practical significance given demonstrate efficiency...

Some results concerning second order expansions for quasidifferentiable functions in the sense of Demyanov and Rubinov whose gradients are represented paper. They similar to those given by Hiriart-Urruty, Strodiot Nguyen (1984).