Solving City Routing Issue with Particle Swarm Optimization

Hadia, Sarman K.; Joshi, A.; Patel, Chaitalee K.; Kosta, Y.P.

doi:10.5120/7266-0348

Cited by 14 publications

(19 citation statements)

References 7 publications

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Section: ) Simulationmentioning

confidence: 99%

“…The performance of the proposed path-planning method is compared with the ones of the PSO-Swap operator in [8], the S-PSO in [9], the S-comprehensive learning PSO (S-CLPSO) in [10], and the S-PSO with chaotic inertia weight in [30]. The inertia weights for previous methods in [8]- [10], [30] in Table 3. The results clearly show that the proposed S-PSO with adaptive inertia weights can plan a pattern with the lowest mean predicted energy with 5.23% lower average in comparison with other methods in [8]- [10], [30].…”

Section: ) Simulationmentioning

confidence: 99%

“…The optimal path planning problem is also an interesting research topic, which has been discussed by many researchers for various applications. Several methods that have been used can be classified into heuristic methods (e.g., Christofides [5] or Concorde [6]) and meta-heuristic methods (e.g., genetic algorithm [7] or discrete particle-swarm-optimization [8]).…”

Section: Introductionmentioning

confidence: 99%

“…However, implementing the PSO to a discrete problem requires some adjustments that have been proposed by several researchers. Hadia et al [8] proposed a swap operator to transform the particle's movement into a swap movement. A set-based particle-swarmoptimization (S-PSO) is proposed by Chen et al [9] to have a more similar characteristic of the continuous-type PSO by transforming the movement in sequence of nodes into sets of paths with probability values.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Neural Network Control and Optimal Path Planning of UAV Surveillance System With Energy Consumption Prediction

Wai

Prasetia

2019

IEEE Access

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A surveillance system is one of the most interesting research topics for an unmanned aerial vehicle (UAV). However, the problem of planning an energy-efficient path for the surveillance purpose while anticipating disturbances and predicting energy consumptions during the path tracking is still a challenging problem in recent years. The optimal path planning and the disturbance rejection control for a UAV surveillance system are investigated in this paper. A trained and tested energy consumption regression model is used to be the cost function of an optimal path planning scheme, which is designed from a clustered 3D real pilot flight pattern with the proposed K-agglomerative clustering method, and is processed via A-star and set-based particle-swarm-optimization (S-PSO) algorithm with adaptive weights. Moreover, an online adaptive neural network (ANN) controller with varied learning rates is designed to ensure the control stability while having a reliably fast disturbance rejection response. The effectiveness of the proposed framework is verified by numerical simulations and experimental results. By applying the proposed optimal path planning scheme, the energy consumption of the optimal path is only 72.3397 Wh while the average consumed energy of real pilot flight data is 96.593Wh. In addition, the proposed ANN control improves average root-meansquare error (RMSE) of horizontal and vertical tracking performance by 49.083% and 37.50% in comparison with a proportional-integral-differential (PID) control and a fuzzy control under the occurrence of external disturbances. According to all of the results, the combination of the proposed optimal path planning scheme and ANN controller can achieve an energy-efficient UAV surveillance systems with fast disturbance rejection response. INDEX TERMS Unmanned aerial vehicle (UAV), optimal path planning, set-based particle-swarmoptimization (S-PSO), adaptive weights, adaptive neural network (ANN), varied learning rates.

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Section: ) Simulationmentioning

confidence: 99%

Section: ) Simulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Adaptive Neural Network Control and Optimal Path Planning of UAV Surveillance System With Energy Consumption Prediction

Wai

Prasetia

2019

IEEE Access

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“…bound to change continuously. OSOMA has been applied on real-time DTSP using a special type of encoding proposed by Hadia et al [12]. A Swap Sequence can be defined as the sequence of Swap Operators.…”

Section: Real-time Dynamic Traveling Salesman Problemmentioning

confidence: 99%

Opportunistic Self Organizing Migrating Algorithm for real-time Dynamic Traveling Salesman Problem

Dokania

Bagga

Sharma

2017

2017 51st Annual Conference on Information Sciences and Systems (CISS)

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Self Organizing Migrating Algorithm (SOMA) is a meta-heuristic algorithm based on the self-organizing behavior of individuals in a simulated social environment. SOMA performs iterative computations on a population of potential solutions in the given search space to obtain an optimal solution. In this paper, an Opportunistic Self Organizing Migrating Algorithm (OSOMA) has been proposed that introduces a novel strategy to generate perturbations effectively. This strategy allows the individual to span across more possible solutions and thus, is able to produce better solutions. A comprehensive analysis of OSOMA on multi-dimensional unconstrained benchmark test functions is performed. OSOMA is then applied to solve real-time Dynamic Traveling Salesman Problem (DTSP). The problem of real-time DTSP has been stipulated and simulated using real-time data from Google Maps with a varying cost-metric between any two cities. Although DTSP is a very common and intuitive model in the real world, its presence in literature is still very limited. OSOMA performs exceptionally well on the problems mentioned above. To substantiate this claim, the performance of OSOMA is compared with SOMA, Differential Evolution and Particle Swarm Optimization.

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Discrete Mother Tree Optimization for the Traveling Salesman Problem

Korani

Mouhoub

2020

Lecture Notes in Computer Science

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Solving City Routing Issue with Particle Swarm Optimization

Cited by 14 publications

References 7 publications

Adaptive Neural Network Control and Optimal Path Planning of UAV Surveillance System With Energy Consumption Prediction

Adaptive Neural Network Control and Optimal Path Planning of UAV Surveillance System With Energy Consumption Prediction

Opportunistic Self Organizing Migrating Algorithm for real-time Dynamic Traveling Salesman Problem

Discrete Mother Tree Optimization for the Traveling Salesman Problem

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