Theory and Applications of Chaotic Optimization Methods

Ikeguchi, Tohru; Hasegawa, Mikio; Kimura, Takayuki; Matsuura, Takafumi; Aihara, Kazuyuki

doi:10.1007/978-3-642-20958-1_8

Cited by 8 publications

(7 citation statements)

References 59 publications

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“…In Eq (23), q j (t) is the number of stored packets of node j at the tth iteration, Cj is the modified transmission performance defined by Eq (6), and θ is a threshold value that determines whether the adjacent node j is congested. Eq (22) works to change the probability of transmitting packets to each of the adjacent nodes adaptively; when the number of stored packets in each adjacent node occupied over 100 × θ% of its buffer size, p 0 ij ðtÞ is recalculated by Eqs ( 22) and ( 23), thereby changing transmitting probabilities so that the packets can be transmitted to adjacent nodes with plenty of free space in their buffer after the iteration passes L in Eq (21). We defined that a node is in the free-flow state when q j ðtÞ= Cj < y and in the congested state when q j ðtÞ= Cj � y.…”

Section: Plos Onementioning

confidence: 99%

“…An advantage of using chaotic neurodynamics is the refractory effect, which inhibits the firing of neurons for a certain period of time [ 21 ]. By using this functionality, the solution methods with the chaotic neurodynamics were applied to various types of combinatorial optimization problems [ 22 ]. In the routing method using the chaotic neurodynamics, the refractory effect works to memorize the histories of packet transmissions at each node.…”

Section: Introductionmentioning

confidence: 99%

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A routing method with adaptively adjusting memory information based on local routing history

Kimura

Shimada

2023

PLoS ONE

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Finding the shortest paths for packets from sources to destinations in packet-switched communication networks is an inevitable problem in building a future high-speed information society. A routing method with memory information has already been proposed to alleviate the congestion of large volumes of packet flow. This routing method shows a high transmission completion rate even for large volumes of packet flows in communication networks with scale-free properties. However, the method exhibits poor performance for networks with local triangular connections and long distances between nodes. To overcome these problems, in this study, we first enhanced the routing performance of the conventional communication network models by using the betweenness centrality of nodes, which is one of the network centralities that measures the number of shortest paths that pass through each node in the networks. Subsequently, we adaptively changed the transmitting paths of packets by using only local information. Numerical simulations indicated that our routing method performs successfully for various topologies of communication networks by avoiding the congested node, and effectively using the memory information.

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Section: Plos Onementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A routing method with adaptively adjusting memory information based on local routing history

Kimura

Shimada

2023

PLoS ONE

Self Cite

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“…The vehicle is serviced within the attention intervals defined by the client, and the waiting time is defined. Based on previous works [50], we propose a mathematical model with probabilistic restrictions that considers the random nature of the problem when establishing a level of service for each customer [38].…”

Section: Problem Statement and Insights From This Studymentioning

confidence: 99%

“…To address the complexity of the VRPTW-ST, Li et al [49] developed a metaheuristic TS-based model, and Ikeguchi et al [50] used a CS method, obtaining better results than the TS method.…”

Section: Problem Statement and Insights From This Studymentioning

confidence: 99%

Vehicle Routing Problem with Deadline and Stochastic Service Times: Case of the Ice Cream Industry in Santiago City of Chile

et al. 2021

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The research evaluates the vehicular routing problem for distributing refrigerated products. The mathematical model corresponds to the vehicle routing problem with hard time windows and a stochastic service time (VRPTW-ST) model applied in Santiago de Chile. For model optimization, we used tabu search, chaotic search and general algebraic modeling. The model’s objective function is to minimize the total distance traveled and the number of vehicles using stochastic waiting restrictions at the customers’ facilities. The experiments were implemented in ten scenarios by modifying the number of customers. Experiments were established with several customers that can be solved using the general algebraic modeling technique in order to validate the tabu search and the chaotic search methods. The study considered two algorithms modified with Monte Carlo (tabu search and chaotic search). Additionally, two modified algorithms, TSv2 and CSv2, were proposed to reduce execution time. These algorithms were modified by delaying the Monte Carlo procedure until the first set of sub-optimal routes were found. The results validate the metaheuristic chaotic search to solve the VRPTW-ST. The chaotic search method obtained a superior performance than the tabu search method when solving a real problem in a large city. Finally, the experiments demonstrated a direct relationship between the percentage of customers with stochastic waiting time and the model resolution time.

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“…However, randomness aspect in the initial population and within the design parameters affects the effectiveness of this algorithm. In order to cope with this issue and according to the "No free lunch theorem [13]", we enhance the original WOA by hybridizing with opposition-based learning [14,15] and chaos theory [16]. The former approach maintains the population diversity when exploring the search space by expanding the search in both directions (random and its opposite direction), and the latter approach preserves a proper transition between exploration and exploitation of search space.…”

Section: Introductionmentioning

confidence: 99%

An efficient population-based multi-objective task scheduling approach in fog computing systems

2021

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With the rapid development of Internet of Things (IoT) technologies, fog computing has emerged as an extension to the cloud computing that relies on fog nodes with distributed resources at the edge of network. Fog nodes offer computing and storage resources opportunities to resource-less IoT devices which are not capable to support IoT applications with computation-intensive requirements. Furthermore, the closeness of fog nodes to IoT devices satisfies the low-latency requirements of IoT applications. However, due to the high IoT task offloading requests and fog resource limitations, providing an optimal task scheduling solution that considers a number of quality metrics is essential. In this paper, we address the task scheduling problem with the aim of optimizing the time and energy consumption as two QoS parameters in the fog context. First, we present a fog-based architecture for handling the task scheduling requests to provide the optimal solutions. Second, we formulate the task scheduling problem as an Integer Linear Programming (ILP) optimization model considering both time and fog energy consumption. Finally, we propose an advanced approach called Opposition-based Chaotic Whale Optimization Algorithm (OppoCWOA) to enhance the performance of the original WOA for solving the modelled task scheduling problem in a timely manner. The efficiency of the proposed OppoCWOA is shown by providing extensive simulations and comparisons with the original WOA and some existing meta-heuristic algorithms such as Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA).

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Theory and Applications of Chaotic Optimization Methods

Cited by 8 publications

References 59 publications

A routing method with adaptively adjusting memory information based on local routing history

A routing method with adaptively adjusting memory information based on local routing history

Vehicle Routing Problem with Deadline and Stochastic Service Times: Case of the Ice Cream Industry in Santiago City of Chile

An efficient population-based multi-objective task scheduling approach in fog computing systems

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