Binary Spring Search Algorithm for Solving Various Optimization Problems

Dehghani, Mohammad; Montazeri, Zeinab; Dehghani, Ali; Malik, O.P.; Nouri, Nima; Ehsanifar, Ali; Guerrero, Josep M.; Ramírez-Mendoza, Ricardo A.

doi:10.3390/app11031286

Cited by 38 publications

(36 citation statements)

References 72 publications

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“…However, this model has several considerable shortcomings, such as inconsistent architectures for different applications, coupled with the process required to tune and fit a neural network, which is a time-consuming procedure that is largely based on trial and error [27,28]. Conventionally, ANNs have been fitted using a backpropagation (BP) algorithm; however, state-of-the-art approaches using bio-inspired, metaheuristic, optimization algorithms have become increasingly prevalent, including the genetic algorithm (GA) [29], particle swarm optimization (PSO) [30], ant lion optimization (ALO) [31], spotted hyena optimizer (SHO) [32], binary spring search algorithm (BSSA) [33], grey wolf algorithm (GWO) [34], genetic optimization resampling based particle filtering (GORPF) algorithm [35], and ant colony optimization (ACO) [16]-all of which may be hybridized with ANNs to address the aforementioned disadvantages.…”

Section: Introductionmentioning

confidence: 99%

Bio-Inspired Hybridization of Artificial Neural Networks: An Application for Mapping the Spatial Distribution of Soil Texture Fractions

et al. 2021

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Soil texture and particle size fractions (PSFs) are a critical characteristic of soil that influences most physical, chemical, and biological properties of soil; furthermore, reliable spatial predictions of PSFs are crucial for agro-ecological modeling. Here, series of hybridized artificial neural network (ANN) models with bio-inspired metaheuristic optimization algorithms such as a genetic algorithm (GA-ANN), particle swarm optimization (PSO-ANN), bat (BAT-ANN), and monarch butterfly optimization (MBO-ANN) algorithms, were built for predicting PSFs for the Mazandaran Province of northern Iran. In total, 1595 composite surficial soil samples were collected, and 64 environmental covariates derived from terrain, climatic, remotely sensed, and categorical datasets were used as predictors. Models were tested using a repeated 10-fold nested cross-validation approach. The results indicate that the hybridized ANN methods were far superior to the reference approach using ANN with a backpropagation training algorithm (BP-ANN). Furthermore, the MBO-ANN approach was consistently determined to be the best approach and yielded the lowest error and uncertainty. The MBO-ANN model improved the predictions in terms of RMSE by 20% for clay, 10% for silt, and 24% for sand when compared to BP-ANN. The physiographical units, soil types, geology maps, rainfall, and temperature were the most important predictors of PSFs, followed by the terrain and remotely sensed data. This study demonstrates the effectiveness of bio-inspired algorithms for improving ANN models. The outputs of this study will support and inform sustainable soil management practices, agro-ecological modeling, and hydrological modeling for the Mazandaran Province of Iran.

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Section: Introductionmentioning

confidence: 99%

Bio-Inspired Hybridization of Artificial Neural Networks: An Application for Mapping the Spatial Distribution of Soil Texture Fractions

et al. 2021

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“…Moreover, the results obtained by BSSA are compared with other standard binary algorithms, namely the grasshopper mechanism, bat algorithm, etc. [20]. The mobile-agent-based system, introduced for IPPS in NMS to prove the consistency of the proposed model comparison, was made with the Controlled Elitist Non-dominated sorting GA [21].…”

Section: Literature Reviewmentioning

confidence: 99%

A Hybrid Multi-Objective Evolutionary Algorithm-Based Semantic Foundation for Sustainable Distributed Manufacturing Systems

et al. 2021

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Rising energy prices, increasing maintenance costs, and strict environmental regimes have augmented the already existing pressure on the contemporary manufacturing environment. Although the decentralization of supply chain has led to rapid advancements in manufacturing systems, finding an efficient supplier simultaneously from the pool of available ones as per customer requirement and enhancing the process planning and scheduling functions are the predominant approaches still needed to be addressed. Therefore, this paper aims to address this issue by considering a set of gear manufacturing industries located across India as a case study. An integrated classifier-assisted evolutionary multi-objective evolutionary approach is proposed for solving the objectives of makespan, energy consumption, and increased service utilization rate, interoperability, and reliability. To execute the approach initially, text-mining-based supervised machine-learning models, namely Decision Tree, Naïve Bayes, Random Forest, and Support Vector Machines (SVM) were adopted for the classification of suppliers into task-specific suppliers. Following this, with the identified suppliers as input, the problem was formulated as a multi-objective Mixed-Integer Linear Programming (MILP) model. We then proposed a Hybrid Multi-Objective Moth Flame Optimization algorithm (HMFO) to optimize process planning and scheduling functions. Numerical experiments have been carried out with the formulated problem for 10 different instances, along with a comparison of the results with a Non-Dominated Sorting Genetic Algorithm (NSGA-II) to illustrate the feasibility of the approach.

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“…The goal in optimization is to determine the values of the variables according to the constraints in order to optimize the objective functions [1]. Population-based optimization algorithms are one of the most widely used methods in solving optimization problems that search the problem-solving space using random operators [2].…”

Section: Introductionmentioning

confidence: 99%

TIMBO: Three Influential Members Based Optimizer

Zeidabadi¹,

Dehghani²,

Malik³

2021

IJIES

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One of the most important and efficient methods in providing suitable solutions for various optimization problems is population-based optimization algorithms. The main contribution and innovation of this paper is to present a new optimization method called Three Influential Members Based Optimizer (TIMBO) which is used for implementation in solving optimization problems. The main idea in designing the proposed TIMBO is to use three important population members with the titles of best member, worst member, and member as mean population in updating the position of population members of the algorithm in the problem search space. The most important feature and advantage of the TIMBO is that it does not have any control parameters, which means that there is no need to control the parameter in this algorithm. TIMBO has been mathematically modeled for use in solving various optimization problems. The efficiency of the TIMBO is analyzed in order to provide suitable quasi-optimal solutions on a set of twenty-three standard objective functions of different types unimodal, high-dimensional multimodal, and fixed-dimensional. Evaluation of unimodal functions indicates the high exploitation power of the proposed TIMBO and evaluation of multimodal functions indicates the appropriate exploration power of the TIMBO. Also, the results obtained from the TIMBO are compared with the performance of eight other well-known optimization algorithms including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Teaching-Learning-Based Optimization (TLBO), Grey Wolf Optimization (GWO), Grasshopper Optimisation Algorithm (GOA), Hide Object Game Optimizer (HOGO), and Flow Direction Algorithm (FDA). The results of optimization of standard objective functions indicate the high capability of the TIMBO in providing quasi-optimal solutions suitable for various optimization problems. In addition, analyzing and comparing the performance of the other eight optimization algorithms shows that the TIMBO has a more effective ability to solve optimization problems and is much more competitive.

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Binary Spring Search Algorithm for Solving Various Optimization Problems

Cited by 38 publications

References 72 publications

Bio-Inspired Hybridization of Artificial Neural Networks: An Application for Mapping the Spatial Distribution of Soil Texture Fractions

Bio-Inspired Hybridization of Artificial Neural Networks: An Application for Mapping the Spatial Distribution of Soil Texture Fractions

A Hybrid Multi-Objective Evolutionary Algorithm-Based Semantic Foundation for Sustainable Distributed Manufacturing Systems

TIMBO: Three Influential Members Based Optimizer

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