A Constrained Consensus Based Optimization algorithm and its Application to Finance

TL;DR: In this article , the authors proposed a multivariate stochastic volatility model with time-delayed interactions, and studied its emergent dynamics, and provided theoretical and numerical solutions of the proposed model and showed that their proposed theoretical framework is sufficient for volatility's exponential convergence toward a constant asymptotic value.

TL;DR: In this paper, the authors proposed a multivariate stochastic volatility model with time-delayed interactions, and studied its emergent dynamics, and provided theoretical and numerical solutions of the proposed model and showed that their proposed theoretical framework is sufficient for volatility's exponential convergence toward a constant asymptotic value.

TL;DR: In this article, a consensus-based optimization (CBO) approach combined with suitable penalization techniques is introduced for high dimensional constrained nonlinear optimization problems by particle-based gradient-free techniques.

TL;DR: The present work expresses the problem as a multi-objective optimization problem and a methodology has been proposed based on multi-objective genetic algo-rithm (MOGA) that exploits the effectiveness of MOGA for searching global optimal solutions in selecting an appropriate image enhancement operator.

TL;DR: This book reviews and introduces the state-of-the-art nature-inspired metaheuristic algorithms in optimization, including genetic algorithms, bee algorithms, particle swarm optimization, simulated annealing, ant colony optimization, harmony search, and firefly algorithms.

TL;DR: This framework encompasses the most common online learning algorithms in use today, as illustrated by several examples, and provides general results describing the convergence of all these learning algorithms at once.