The Effect of Entropy on the Performance of Modified Genetic Algorithm Using Earthquake and Wind Time Series

Vargas, Manuel; Fuertes, Guillermo; Alfaro, Miguel; Gatica, Gustavo; Gutiérrez, Sebastián; Peralta, María Alejandra

doi:10.1155/2018/4392036

Cited by 12 publications

(5 citation statements)

References 53 publications

(49 reference statements)

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“…As far as entropy is concerned, it can be calculated on the basis of the problem data, as Vargas et al. (2018) did. The authors asserted a positive correlation between the entropy of the initial populations and the performance of a GA while solving the problem at hand.…”

Section: Randomizationmentioning

confidence: 99%

Initialization of metaheuristics: comprehensive review, critical analysis, and research directions

Sarhani

Voß

Jovanović

2022

Int Trans Operational Res

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Initialization of metaheuristics is a crucial topic that lacks a comprehensive and systematic review of the state of the art. Providing such a review requires in‐depth study and knowledge of the advances and challenges in the broader field of metaheuristics, especially with regard to diversification strategies, in order to assess the proposed methods and provide insights for initialization. Motivated by the aforementioned research gap, we provide a related review and begin by describing the main metaheuristic methods and their diversification mechanisms. Then, we review and analyze the existing initialization approaches while proposing a new categorization of them. Next, we focus on challenging optimization problems, namely constrained and discrete optimization. Lastly, we give insights on the initialization of local search approaches.

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Section: Randomizationmentioning

confidence: 99%

Initialization of metaheuristics: comprehensive review, critical analysis, and research directions

Sarhani

Voß

Jovanović

2022

Int Trans Operational Res

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“…Following this line of research, in [ 45 ], a hybrid algorithm between Shannon entropy and two swarm methods is introduced to improve the yield, memory, velocity, and, consequently, the move update. In [ 46 ], Shannon entropy is integrated into a chaotic genetic algorithm for taking data from solutions generated during the execution. This process runs in deterministic time series and operates from the initial population strategy.…”

Section: Related Workmentioning

confidence: 99%

Entropy–Based Diversification Approach for Bio–Computing Methods

Olivares

Soto

Crawford

et al. 2022

Entropy

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Nature–inspired computing is a promising field of artificial intelligence. This area is mainly devoted to designing computational models based on natural phenomena to address complex problems. Nature provides a rich source of inspiration for designing smart procedures capable of becoming powerful algorithms. Many of these procedures have been successfully developed to treat optimization problems, with impressive results. Nonetheless, for these algorithms to reach their maximum performance, a proper balance between the intensification and the diversification phases is required. The intensification generates a local solution around the best solution by exploiting a promising region. Diversification is responsible for finding new solutions when the main procedure is trapped in a local region. This procedure is usually carryout by non-deterministic fundamentals that do not necessarily provide the expected results. Here, we encounter the stagnation problem, which describes a scenario where the search for the optimum solution stalls before discovering a globally optimal solution. In this work, we propose an efficient technique for detecting and leaving local optimum regions based on Shannon entropy. This component can measure the uncertainty level of the observations taken from random variables. We employ this principle on three well–known population–based bio–inspired optimization algorithms: particle swarm optimization, bat optimization, and black hole algorithm. The proposal’s performance is evidenced by solving twenty of the most challenging instances of the multidimensional knapsack problem. Computational results show that the proposed exploration approach is a legitimate alternative to manage the diversification of solutions since the improved techniques can generate a better distribution of the optimal values found. The best results are with the bat method, where in all instances, the enhanced solver with the Shannon exploration strategy works better than its native version. For the other two bio-inspired algorithms, the proposal operates significantly better in over 70% of instances.

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“…Vargas et al [48] studied the effect of entropy on the performance of the genetic algorithm modified using a time series of earthquakes and winds. In this study, the genetic algorithm modifies the stochastic parameters of the genetic algorithm with the chaotic sequences of numbers from the earthquake and wind time series.…”

Section: Introductionmentioning

confidence: 99%

Chaotic Honeybees Optimization Algorithms Approach for Traveling Salesperson Problem

et al. 2022

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Due to the difficulty in solving combinatorial optimization problems, it is necessary to improve the performance of the algorithms by improving techniques to deal with complex optimizations. This research addresses the metaheuristics of marriage in honey-bees optimization (MBO) based on the behavior of bees. The current study proposes a technique for solving combinatorial optimization problems within proper computation times. The purpose of this study focuses on the travelling salesperson problem and the application of chaotic methods in important sections of the MBO metaheuristic. Three experiments were conducted to measure the efficiency and quality of the solutions: (1) MBO with chaos to generate initial solutions (MBO2); (2) MBO with chaos in the workers (MBO3); and (3) MBO with chaos to generate initial solutions and the workers (MBO4). The application of chaotic functions in MBO was significantly better at solving the travelling salesperson problem.

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The Effect of Entropy on the Performance of Modified Genetic Algorithm Using Earthquake and Wind Time Series

Cited by 12 publications

References 53 publications

Initialization of metaheuristics: comprehensive review, critical analysis, and research directions

Initialization of metaheuristics: comprehensive review, critical analysis, and research directions

Entropy–Based Diversification Approach for Bio–Computing Methods

Chaotic Honeybees Optimization Algorithms Approach for Traveling Salesperson Problem

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