Multi-objective optimization design of the wind-to-heat system blades based on the Particle Swarm Optimization algorithm

Qian, Jing; Sun, Xiangyu; Zhong, Xiaohui; Zeng, Jiajun; Xu, Fei; Zhou, Teng; Shi, Kezhong; Li, Qingan

doi:10.1016/j.apenergy.2023.122186

Cited by 10 publications

(1 citation statement)

References 19 publications

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“…To this end, Gragnaniello et al [ 45 ] explored brute-force multi-objective optimization for thermal management applications, while Bianco et al [ 46 ] leveraged a genetic algorithm for optimizing a heat recovery and ventilation unit’s design, exemplifying the utility of multi-objective optimization in yielding non-dominated solutions across varied domains [ 25 , 31 , 33 , 36 , 37 , 38 ]. Particle swarm optimization has been prominently featured in solving multi-objective issues, including diverse applications, from vehicle routing problems [ 29 ] to wind turbine optimization [ 47 ]. Despite their advantages, genetic algorithms often face slow convergence, and particle swarm optimization may encounter local optimization pitfalls.…”

Section: Introductionmentioning

confidence: 99%

A Grey Wolf Optimizer Algorithm for Multi-Objective Cumulative Capacitated Vehicle Routing Problem Considering Operation Time

Huang,

Qi,

Cai

et al. 2024

Biomimetics

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In humanitarian aid scenarios, the model of cumulative capacitated vehicle routing problem can be used in vehicle scheduling, aiming at delivering materials to recipients as quickly as possible, thus minimizing their wait time. Traditional approaches focus on this metric, but practical implementations must also consider factors such as driver labor intensity and the capacity for on-site decision-making. To evaluate driver workload, the operation times of relief vehicles are typically used, and multi-objective modeling is employed to facilitate on-site decision-making. This paper introduces a multi-objective cumulative capacitated vehicle routing problem considering operation time (MO-CCVRP-OT). Our model is bi-objective, aiming to minimize both the cumulative wait time of disaster-affected areas and the extra expenditures incurred by the excess operation time of rescue vehicles. Based on the traditional grey wolf optimizer algorithm, this paper proposes a dynamic grey wolf optimizer algorithm with floating 2-opt (DGWO-F2OPT), which combines real number encoding with an equal-division random key and ROV rules for decoding; in addition, a dynamic non-dominated solution set update strategy is introduced. To solve MO-CCVRP-OT efficiently and increase the algorithm’s convergence speed, a multi-objective improved floating 2-opt (F2OPT) local search strategy is proposed. The utopia optimum solution of DGWO-F2OPT has an average value of two fitness values that is 6.22% lower than that of DGWO-2OPT. DGWO-F2OPT’s average fitness value in the algorithm comparison trials is 16.49% less than that of NS-2OPT. In the model comparison studies, MO-CCVRP-OT is 18.72% closer to the utopian point in Euclidean distance than CVRP-OT.

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