2017
DOI: 10.3390/app7050469
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Multi-Objective Climb Path Optimization for Aircraft/Engine Integration Using Particle Swarm Optimization

Abstract: Abstract:In this article, a new multi-objective approach to the aircraft climb path optimization problem, based on the Particle Swarm Optimization algorithm, is introduced to be used for aircraft-engine integration studies. This considers a combination of a simulation with a traditional Energy approach, which incorporates, among others, the use of a proposed path-tracking scheme for guidance in the Altitude-Mach plane. The adoption of population-based solver serves to simplify case setup, allowing for direct i… Show more

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Cited by 17 publications
(9 citation statements)
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“…A number of particles which fly around in the search space to find the best solution are used in PSO [35][36][37][38]. Each particle denotes a candidate solution for optimization problems, which has two characteristics: position and velocity.…”
Section: Improved Particle Swarm Optimizationmentioning
confidence: 99%
“…A number of particles which fly around in the search space to find the best solution are used in PSO [35][36][37][38]. Each particle denotes a candidate solution for optimization problems, which has two characteristics: position and velocity.…”
Section: Improved Particle Swarm Optimizationmentioning
confidence: 99%
“…One year later, in 2017 another publication [40] reviews robot path planning techniques, with soft computing and heuristic approaches (artificial neural networks, fuzzy logic, wavelets, and genetic algorithms) replacing the classical methods (potential fields, roadmap, and cell decomposition). In same year, another work [41] addressed the path planning for the problem of an aircraft climbing using PSO and a two-level optimization scheme, in order to improve search performance, with results showing 15% faster climbs saving 20% more fuel.…”
Section: Multi-objective Path Planningmentioning
confidence: 99%
“…where ω is the inertia coefficient, x gbest is the global best output of the particle, x pbest is the personal best output of the particle, c 1 is the cognitive acceleration coefficient, c 2 is the social acceleration coefficient, r 1 , r 2 are random numbers, V t+1 i is the velocity at time t + 1, and X t+1 i is the position at time t + 1. The MPSO algorithm encounters a set of solutions as a Pareto front [38]. The archive of a non-dominated solution is considered in the solutions developed at each level.…”
Section: Multi-objective Particle Swarm Optimization (Mpso)mentioning
confidence: 99%