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A Particle Swarm Inspired Approach for Continuous Distributed Constraint Optimization Problems
To address this issue, we propose a new C-DCOP algorithm, namely Particle Swarm Optimization Based C-DCOP (PCD), which is inspired by Particle Swarm Optimization (PSO), a well-known centralized population-based approach for solving continuous optimization problems.
Learning Optimal Temperature Region for Solving Mixed Integer Functional DCOPs
Distributed Constraint Optimization Problems (DCOPs) are an important framework for modeling coordinated decision-making problems in multi-agent systems with a set of discrete variables.
C-CoCoA: A Continuous Cooperative Constraint Approximation Algorithm to Solve Functional DCOPs
To overcome this limitation, Functional DCOP (F-DCOP) model is proposed that is able to model problems with continuous variables.
AED: An Anytime Evolutionary DCOP Algorithm
Evolutionary optimization is a generic population-based metaheuristic that can be adapted to solve a wide variety of optimization problems and has proven very effective for combinatorial optimization problems.
