AIMC Topic: Problem Solving

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Chaotic Teaching-Learning-Based Optimization with Lévy Flight for Global Numerical Optimization.

Computational intelligence and neuroscience
Recently, teaching-learning-based optimization (TLBO), as one of the emerging nature-inspired heuristic algorithms, has attracted increasing attention. In order to enhance its convergence rate and prevent it from getting stuck in local optima, a nove...

Solving the Traveling Salesman's Problem Using the African Buffalo Optimization.

Computational intelligence and neuroscience
This paper proposes the African Buffalo Optimization (ABO) which is a new metaheuristic algorithm that is derived from careful observation of the African buffalos, a species of wild cows, in the African forests and savannahs. This animal displays unc...

Particle Swarm Optimization with Double Learning Patterns.

Computational intelligence and neuroscience
Particle Swarm Optimization (PSO) is an effective tool in solving optimization problems. However, PSO usually suffers from the premature convergence due to the quick losing of the swarm diversity. In this paper, we first analyze the motion behavior o...

A Bi-Projection Neural Network for Solving Constrained Quadratic Optimization Problems.

IEEE transactions on neural networks and learning systems
In this paper, a bi-projection neural network for solving a class of constrained quadratic optimization problems is proposed. It is proved that the proposed neural network is globally stable in the sense of Lyapunov, and the output trajectory of the ...

Behavioral plasticity through the modulation of switch neurons.

Neural networks : the official journal of the International Neural Network Society
A central question in artificial intelligence is how to design agents capable of switching between different behaviors in response to environmental changes. Taking inspiration from neuroscience, we address this problem by utilizing artificial neural ...

Combining Computational and Social Effort for Collaborative Problem Solving.

PloS one
Rather than replacing human labor, there is growing evidence that networked computers create opportunities for collaborations of people and algorithms to solve problems beyond either of them. In this study, we demonstrate the conditions under which s...

A non-penalty recurrent neural network for solving a class of constrained optimization problems.

Neural networks : the official journal of the International Neural Network Society
In this paper, we explain a methodology to analyze convergence of some differential inclusion-based neural networks for solving nonsmooth optimization problems. For a general differential inclusion, we show that if its right hand-side set valued map ...

Off-line simulation inspires insight: A neurodynamics approach to efficient robot task learning.

Neural networks : the official journal of the International Neural Network Society
There is currently an increasing demand for robots able to acquire the sequential organization of tasks from social learning interactions with ordinary people. Interactive learning-by-demonstration and communication is a promising research topic in c...

Nonlinear Inertia Weighted Teaching-Learning-Based Optimization for Solving Global Optimization Problem.

Computational intelligence and neuroscience
Teaching-learning-based optimization (TLBO) algorithm is proposed in recent years that simulates the teaching-learning phenomenon of a classroom to effectively solve global optimization of multidimensional, linear, and nonlinear problems over continu...

Reinforcement learning solution for HJB equation arising in constrained optimal control problem.

Neural networks : the official journal of the International Neural Network Society
The constrained optimal control problem depends on the solution of the complicated Hamilton-Jacobi-Bellman equation (HJBE). In this paper, a data-based off-policy reinforcement learning (RL) method is proposed, which learns the solution of the HJBE a...