AIMC Topic: Problem Solving

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A Novel Teaching-Learning-Based Optimization with Error Correction and Cauchy Distribution for Path Planning of Unmanned Air Vehicle.

Computational intelligence and neuroscience
Teaching-learning-based optimization (TLBO) algorithm is a novel heuristic method which simulates the teaching-learning phenomenon of a classroom. However, in the later period of evolution of the TLBO algorithm, the lower exploitation ability and the...

Learning to activate logic rules for textual reasoning.

Neural networks : the official journal of the International Neural Network Society
Most current textual reasoning models cannotlearn human-like reasoning process, and thus lack interpretability and logical accuracy. To help address this issue, we propose a novel reasoning model which learns to activate logic rules explicitly via de...

Visual mental imagery: A view from artificial intelligence.

Cortex; a journal devoted to the study of the nervous system and behavior
This article investigates whether, and how, an artificial intelligence (AI) system can be said to use visual, imagery-based representations in a way that is analogous to the use of visual mental imagery by people. In particular, this article aims to ...

Representing, Running, and Revising Mental Models: A Computational Model.

Cognitive science
People use commonsense science knowledge to flexibly explain, predict, and manipulate the world around them, yet we lack computational models of how this commonsense science knowledge is represented, acquired, utilized, and revised. This is an import...

Fuzzy-Rough Cognitive Networks.

Neural networks : the official journal of the International Neural Network Society
Rough Cognitive Networks (RCNs) are a kind of granular neural network that augments the reasoning rule present in Fuzzy Cognitive Maps with crisp information granules coming from Rough Set Theory. While RCNs have shown promise in solving different cl...

The Artificial Neural Networks Based on Scalarization Method for a Class of Bilevel Biobjective Programming Problem.

Computational intelligence and neuroscience
A two-stage artificial neural network (ANN) based on scalarization method is proposed for bilevel biobjective programming problem (BLBOP). The induced set of the BLBOP is firstly expressed as the set of minimal solutions of a biobjective optimization...

A biologically inspired controller to solve the coverage problem in robotics.

Bioinspiration & biomimetics
The coverage problem consists on computing a path or trajectory for a robot to pass over all the points in some free area and has applications ranging from floor cleaning to demining. Coverage is solved as a planning problem-providing theoretical val...

HSTLBO: A hybrid algorithm based on Harmony Search and Teaching-Learning-Based Optimization for complex high-dimensional optimization problems.

PloS one
Harmony Search (HS) and Teaching-Learning-Based Optimization (TLBO) as new swarm intelligent optimization algorithms have received much attention in recent years. Both of them have shown outstanding performance for solving NP-Hard optimization proble...

Exploiting Linkage Information and Problem-Specific Knowledge in Evolutionary Distribution Network Expansion Planning.

Evolutionary computation
This article tackles the Distribution Network Expansion Planning (DNEP) problem that has to be solved by distribution network operators to decide which, where, and/or when enhancements to electricity networks should be introduced to satisfy the futur...