Category: Projects

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(Constrained) Numerical Optimization Problem (NOP)

Category : Code

A general constrained numerical optimization problem (NOP) may be written as follows: where is the objective function and each is a constraint function to be satisfied, and and are constants. Each function can be nonlinear and non-smooth. If there is no constraint (i.e., ), then the problem becomes an unconstrained optimization problem. NOP Instance Implementation

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MAOS-TSP: Project Portal

MAOS-TSP [1] is a multiagent optimization system (MAOS) for solving the Traveling Salesman Problem (TSP). Related Information: Please find other related code and software in our Source Code Library. Basic Description What’s New Directories & Files Command Line & Parameters Output Information References Contact License Information: You can redistribute and/or modify it under the terms

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MAOS-GCP: Project Portal

MAOS-GCP [1] is a multiagent optimization system (MAOS) for solving the Graph Coloring Problem (GCP). Related Information: MAOS-GCP shares the MAOS kernel with other MAOS applications (e.g. MAOS-TSP, MAOS-FSP, MAOS-QAP, MAOS-QKP), and contains some modules that are specifically for tacking GCP. Please find other related code and software in our Source Code Library. Basic Description What’s

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MAOS-QKP: Project Portal

MAOS-QKP is a multiagent optimization system (MAOS) for solving the Quadratic Knapsack Problem (QKP). Related Information: MAOS-QKP shares the MAOS kernel with other MAOS applications (e.g. Graph Coloring Problem (GCP) and Traveling Salesman Problem (TSP)). Please find other related code and software in our Source Code Library. Basic Description What’s New Directories & Files Command Line

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MAOS-FSP: Project Portal

MAOS-FSP [1] is a multiagent optimization system (MAOS) for solving the Flowshop Scheduling Problem (FSP). MAOS-FSP shares the MAOS kernel with other MAOS applications (e.g. MAOS-GCP and MAOS-TSP), and contains some modules that are specifically for tacking FSP. Related Information: Please find other related code and software in our Source Code Library. Basic Description What’s

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MAOS-QAP: Project Portal

MAOS-QAP [1] is a cooperative group optimization system (MAOS) for solving the Quadratic Assignment Problem (QAP). Related Information: MAOS-QAP shares the MAOS kernel with other MAOS applications (e.g. MAOS-GCP and MAOS-TSP), and contains some modules that are specifically for tacking QAP. Please find other related code and software in our Source Code Library. Basic Description

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Ant Colony Optimization (ACO) Algorithms

Ant colony optimization (ACO), or ant system (AS), is a class of metaheuristic optimization algorithms inspired by the emergent search behavior using pheromone trails in natural ants. We present CGO-AS, a generalized ant system (AS) implemented in the framework of cooperative group optimization (CGO), to show the leveraged optimization with a mixed individual and social

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Cooperative Group Optimization System (CGO)

Category : Projects

The cooperative group optimization (CGO) system consists of a group of intelligent agents cooperating with their peers in a sharing environment for realizing a common intention of finding high-quality solution(s) based on the landscape representation of an optimization task. Numerical Optimization CGO has also been applied on numerical optimization problem (NOP) to find solutions in high-dimensional nonlinear continuous space.

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Smart and Scalable Urban Signal Networks

This system is a real-time adaptive traffic control system, which combines artificial intelligence (AI) and traffic theory to optimize highly dynamic traffic flow in urban road networks. The system (smart, adaptable traffic signals) is listed as 25 Great Things about Computer Science at Carnegie Mellon. Xiao-Feng Xie, et al. Smart and Scalable Urban Signal Networks: Methods

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Social Cognitive Optimization (SCO): Project Portal

Social Cognitive Optimization (SCO) [1, 2] is an optimization algorithm for solving the (constrained) numerical optimization problem. SCO is an agent-based model based on the observational learning mechanism in human social cognition. In CGOS [3], SCO was hybridized with differential evolution (DE) to obtain better results than individual algorithms on a common set of benchmark

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