A particle swarm optimization algorithm for optimal car-call allocation in elevator group control systems

Bolat, Berna; Altun, Oğuz; Cortés, Pablo

doi:10.1016/j.asoc.2012.11.023

Cited by 32 publications

(11 citation statements)

References 16 publications

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“…The soil model parameters were determined in an iterative optimization loop with PSO and an adaptive network based on a fuzzy inference system such that the equations of the linear elastic model and (where appropriate) the hardening Drucker-Prager yielded criterion are simultaneously satisfied. Bolat et al [291] developed a particle swarm optimization (PSO) algorithm to deal with car-call allocation problem. In vertical transportation, when a passenger makes a hall call by pressing a landing call button installed at the floor and located near the cars of the elevator group, the complex-elevator-group-control system must allocate one of the cars of the group to the hall call.…”

Section: Civil Engineeringmentioning

confidence: 99%

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Zhang

Wang

2015

Mathematical Problems in Engineering

683

380

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Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms.

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Section: Civil Engineeringmentioning

confidence: 99%

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Zhang

Wang

2015

Mathematical Problems in Engineering

683

380

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“…In terms of optimizing control algorithms to improve energy efficiency [17,18], several types are involved, which include particle swarm optimization algorithms [19], viral system algorithms [20], energy-efficient group scheduling [21], and sensor-aware elevator scheduling [22].…”

Section: Major Methods Of Elevator Energy Conservationmentioning

confidence: 99%

A Novel Reconstruction Approach to Elevator Energy Conservation Based on a DC Micro-Grid in High-Rise Buildings

et al. 2018

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Elevators were reported to cause an important part of building energy consumption. In general, each elevator has two operation states: The load state and power regeneration state. During operation, it has the potential to save energy by using regeneration power efficiently. In existing research, a set of energy storage devices are installed for every elevator, which is highly costly. In this paper, an energy conservation approach for elevators based on a direct current (DC) micro-grid is proposed, which has better economy. Then, an innovative energy-efficient device for the elevator group is designed based on a supercapacitor with similar characteristics and lifetimes. In a high-rise building case study, the experimental test and field data collection show that the innovative approach could result in a high energy efficiency within 15.87–23.1% and 24.1–54.5%, respectively. It is expected that the proposed method and designed device could be employed practically, saving energy consumption for elevator reconstruction.

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“…Next, it ascertains the discrete Markov equivalent problem that is solved by dynamic programming. Particle swarm algorithms may produce better results than genetic algorithms or tabu search engines [85]. In particle swarm algorithms, each solution is encoded as a particle that is placed in the space of possible solutions and has its own associated speed that defines its movement through the solution space.…”

Section: Other Computational Approachesmentioning

confidence: 99%

A Survey of Elevator Group Control Systems for Vertical Transportation: A Look at Recent Literature

Fernández

Cortés

2015

IEEE Control Syst.

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A particle swarm optimization algorithm for optimal car-call allocation in elevator group control systems

Cited by 32 publications

References 16 publications

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

A Novel Reconstruction Approach to Elevator Energy Conservation Based on a DC Micro-Grid in High-Rise Buildings

A Survey of Elevator Group Control Systems for Vertical Transportation: A Look at Recent Literature

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