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Our framework is based on swarm intelligence and agent-based modeling, which allows us to create a large number of people with specific behavioral characteristics.
Particle swarm optimization (PSO) is one of the popular stochastic optimization based on swarm intelligence algorithm.
Recently algorithms based on Swarm Intelligence (SI) have gained prominence due to the underlying focus on collective intelligent behavior.
Unilever, for instance, has used a computer program based on swarm intelligence to organise movements between storage tanks, mixers and packing lines in one of its factories.
It has been developed based on swarm intelligence and the peculiar behavior of dragonflies in nature.
Particle swarm optimization (PSO) is a powerful stochastic evolutionary algorithm based on swarm intelligence.
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The emphasis is on optimal clusters and locations of these clusters to maximize the multi-hop reach along the line based on the swarm intelligence algorithms which are the Glowworm Swarm Optimization (GSO) and Improved Ant Colony Algorithm (IACA).
The paper presents a new approach to solve FFPs based on two Swarm Intelligence (SI) algorithms.
To find a numerical solution of the involved mathematical programming problem, we adopt an evolutionary algorithm based on the particle swarm optimization (PSO), which is an iterative metaheuristics grounded on swarm intelligence.
The goal of this paper is to propose a principled and methodological approach for the engineering of systems based upon swarm intelligence.
In this paper three heuristic techniques based in swarm intelligence are studied, namely its ability to solve non-convex, non-differentiable and highly restricted optimization problems.
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