An elitist seasonal artificial bee colony algorithm for the interval job shop

Díaz, Hernán; Palacios, Juan José; González-Rodríguez, Inés; Vela, Camino R.

doi:10.3233/ica-230705

Cited by 5 publications

(8 citation statements)

References 58 publications

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“…So sooner or later it would enter the uncanny valley or be detected. An hybrid approach is a possibility to consider [90]. Since GPTZero requires a minimum of 250 characters, the ChatGPT was asked for rewriting the tweet for 280 characters (the maximum allowed length in Twitter).…”

Section: Llm-based Approachmentioning

confidence: 99%

An exploratory design science research on troll factories

Marcondes,

Almeida,

Novais

2023

ICA

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Private and military troll factories (facilities used to spread rumours in online social media) are currently proliferating around the world. By their very nature, they are obscure companies whose internal workings are largely unknown, apart from leaks to the press. They are even more concealed when it comes to their underlying technology. At least in a broad sense, it is believed that there are two main tasks performed by a troll factory: sowing and spreading. The first is to create and, more importantly, maintain a social network that can be used for the spreading task. It is then a wicked long-term activity, subject to all sorts of problems. As an attempt to make this perspective a little clearer, this paper uses exploratory design science research to produce artefacts that could be applied to online rumour spreading in social media. Then, as a hypothesis: it is possible to design a fully automated social media agent capable of sowing a social network on microblogging platforms. The expectation is that it will be possible to identify common opportunities and difficulties in the development of such tools, which in turn will allow an evaluation of the technology, but above all the level of automation of these facilities. The research is based on a general domain Twitter corpus with 4M+ tokens and on ChatGPT, and discusses both knowledge-based and deep learning approaches for smooth tweet generation. These explorations suggest that for the current, widespread and publicly available NLP technology, troll factories work like a call centre; i.e. humans assisted by more or less sophisticated computing tools (often called cyborgs).

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Section: Llm-based Approachmentioning

confidence: 99%

An exploratory design science research on troll factories

Marcondes,

Almeida,

Novais

2023

ICA

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“…Many research works often independently study job shop scheduling problems and vehicle transportation problems, such as dynamic job shop scheduling [12,17], interval job shop [13], energy-efficient distributed flexible job shop scheduling [14], limited waiting time constraint on a hybrid flowshop [18], embedded environment [19], and flexible job shop scheduling [20]. Ref.…”

Section: Literature Reviewmentioning

confidence: 99%

“…It is very important and challenging to design efficient algorithms to address it in large-sized cases, such as simulated annealing (SA) [6] and fuzzy logic (FL) [11]. Among them, swarm intelligence (SI) algorithms have received great attention [23,26], i.e., genetic algorithms (GAs) [2,4,16], particle swarm optimiza-tion (PSO) [6,19], ant colony optimization (ACO) [8], deep learning (DL), artificial neural networks (ANNs) [12,27], artificial bee colony (ABC) [13], adaptive memetic algorithms (AMAs) [14], migrating birds optimization [17], grey wolf optimization (GWO) [20], quantum cat swarm optimization [22], artificial slime mold [28], artificial Physarum swarm [29], coronavirus herd immunity [30], artificial plant community [31,32], whale optimization [33], artificial algae [34], and the Jaya algorithm [35]. However, these swarm intelligence algorithms are also prone to fall into local optimization prematurely, and some scholars have tried to improve algorithm performance using hybrid algorithms [6,36].…”

Section: Literature Reviewmentioning

confidence: 99%

“…In our experiments, the proposed APPC was compared with a genetic algorithm (GA) [2,4,16], particle swarm optimization (PSO) [6,19], ant colony optimization (ACO) [8], deep learning (DL) [12,27], and an artificial bee colony (ABC) [13]. It was assumed that all algorithms employed the same population size S and iteration steps Ite_max to solve the same flexible job shop scheduling problem with transportation constraints.…”

Section: Experimental Environmentmentioning

confidence: 99%

“…The latter is also called a transportation scheduling problem or a load/unload problem. Many researchers have proposed all kinds of artificial intelligence algorithms to solve these kinds of complex problems, i.e., genetic algorithms (GAs) [2,4], particle swarm optimization (PSO) [6], simulated annealing (SA) [6], ant colony optimization (ACO) [8], fuzzy logic (FL) [11], deep learning (DL), artificial neural networks (ANNs) [12], artificial bee colony (ABC) [13], and adaptive memetic algorithms (AMAs) [14]. The experimental results of these studies revealed that these artificial intelligence algorithms can obtain satisfactory results in job shop scheduling problems.…”

Section: Introductionmentioning

confidence: 99%

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A State Transition Diagram and an Artificial Physarum polycephalum Colony Algorithm for the Flexible Job Shop Scheduling Problem with Transportation Constraints

Cai,

Feng,

Yang

et al. 2023

Processes

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In many flexible job shop scheduling problems, transportation scheduling problems are involved, increasing the difficulty in problem-solving. Here, a novel artificial Physarum polycephalum colony algorithm is proposed to help us address this problem. First, the flexible job shop scheduling problem with transportation constraints is modeled as a state transition diagram and a multi-objective function, where there are ten states in total for state transition, and the multi-objective function considers the makespan, average processing waiting time, and average transportation waiting time. Second, a novel artificial Physarum polycephalum colony algorithm is designed herein with two main operations: expansion and contraction. In the expansion operation, each mycelium can cross with any other mycelia and generate more offspring mycelia, of which each includes multiple pieces of parental information, so the population expands to more than twice its original size. In the contraction operation, a fast grouping section algorithm is designed to randomly group all mycelia according to the original population size, where each group selects the best fitness one to survive, but the other mycelia are absorbed to disappear, so the population size recovers to the original size. After multiple iterations, the proposed algorithm can find the optimal solution to the flexible job shop scheduling problem. Third, a series of computational experiments are conducted on several benchmark instances, and a selection of mainstream algorithms is employed for comparison. These experiments revealed that the proposed method outperformed many state-of-the-art algorithms and is very promising in helping us to solve these complex problems.

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