Multi-population differential evolution-assisted Harris hawks optimization: Framework and case studies

Optimization of support vector machine through the use of metaheuristic algorithms in forecasting TBM advance rate

This paper thoroughly presents a comprehensive review of the so-called moth–flame optimization (MFO) and analyzes its main characteristics. MFO is considered one of the promising metaheuristic algorithms and successfully applied in various optimization problems in a wide range of fields, such as power and energy systems,
economic dispatch, engineering design, image processing and medical applications. This manuscript describes the available literature on MFO, including its variants and hybridization, the growth of MFO publications, MFO application areas, theoretical analysis and comparisons of MFO with other algorithms.
Conclusions focus on the current work on MFO, highlight its weaknesses, and suggest possible future research directions. Researchers and practitioners of MFO belonging to different fields, like the domains of optimization, medical, engineering, clustering and data mining, among others will benefit from this study.

Moth–flame optimization algorithm: variants and applications

Since different features may require different costs, the cost-sensitive feature selection problem become more and more important in real-world applications. Generally, it includes two main conflicting objectives, i.e., maximizing the classification performance and minimizing the feature cost. However, most existing approaches treat this task as a single-objective optimization problem. To satisfy various requirements of decision-makers, this paper studies a multi-objective feature selection approach, called two-archive multi-objective artificial bee colony algorithm (TMABC-FS). Two new operators, i.e., convergence-guiding search for employed bees and diversity-guiding search for onlooker bees, are proposed for obtaining a group of non-dominated feature subsets with good distribution and convergence. And two archives, i.e., the leader archive and the external archive are employed to enhance the search capability of different kinds of bees. The proposed TMABC-FS is validated on several datasets from UCI, and is compared with two traditional algorithms and three multi-objective methods. Results have shown that TMABC-FS is an efficient and robust optimization method for solving cost-sensitive feature selection problems.

Cost-sensitive feature selection using two-archive multi-objective artificial bee colony algorithm

A reliable and accurate prediction of the tunnel boring machine (TBM) performance can assist in minimizing the relevant risks of high capital costs and in scheduling tunneling projects. This research aims to develop six hybrid models of extreme gradient boosting (XGB) which are optimized by gray wolf optimization (GWO), particle swarm optimization (PSO), social spider optimization (SSO), sine cosine algorithm (SCA), multi verse optimization (MVO) and moth flame optimization (MFO), for estimation of the TBM penetration rate (PR). To do this, a comprehensive database with 1286 data samples was established where seven parameters including the rock quality designation, the rock mass rating, Brazilian tensile strength (BTS), rock mass weathering, the uniaxial compressive strength (UCS), revolution per minute and trust force per cutter (TFC), were set as inputs and TBM PR was selected as model output. Together with the mentioned six hybrid models, four single models i.e., artificial neural network, random forest regression, XGB and support vector regression were also built to estimate TBM PR for comparison purposes. These models were designed conducting several parametric studies on their most important parameters and then, their performance capacities were assessed through the use of root mean square error, coefficient of determination, mean absolute percentage error, and a10-index. Results of this study confirmed that the best predictive model of PR goes to the PSO-XGB technique with system error of (0.1453, and 0.1325), R2 of (0.951, and 0.951), mean absolute percentage error (4.0689, and 3.8115), and a10-index of (0.9348, and 0.9496) in training and testing phases, respectively. The developed hybrid PSO-XGB can be introduced as an accurate, powerful and applicable technique in the field of TBM performance prediction. By conducting sensitivity analysis, it was found that UCS, BTS and TFC have the deepest impacts on the TBM PR.

Predicting TBM penetration rate in hard rock condition: A comparative study among six XGB-based metaheuristic techniques

The product design process has a very important effect on product costs. It aims to develop products that are able to compete by achieving an optimum design in the product design process. This research presents the first application in the literature of the grey wolf, whale, water cycle, ant lion and sine-cosine optimization algorithms for the optimum design of vehicle components. In this study, the optimal structural model of a vehicle connecting rod was determined. In the optimization process, various design alternatives were created by using the latin hypercube method. Stress analysis was performed for each of these designs. According to the generated responses, equations for objective and constraint functions were obtained. The optimization problem was solved using the above mentioned algorithms which have been newly developed in the literature, resulting in optimum connection rod design. The results demonstrate that the grey wolf, whale, water cycle, ant lion and sine-cosine algorithms are v...

Comparison of grey wolf, whale, water cycle, ant lion and sine-cosine algorithms for the optimization of a vehicle engine connecting rod

In this research, a newly developed moth-flame optimization algorithm (MFO) is presented for solving optimization problems in manufacturing industry. A well-known milling optimization problem is solved to emphasize the effectiveness of the MFO in the optimization of manufacturing problems. In the optimization problem solved in this paper, the main aim is to maximize the profit rate for multi-tool milling operations considering difficult constraints. The results demonstrate that the MFO is an effective optimization method for the solution of manufacturing optimization problems.

Moth-flame optimization algorithm to determine optimal machining parameters in manufacturing processes

In this paper, a novel hybrid optimization algorithm (H-HHONM) which combines the Nelder-Mead local search algorithm with the Harris hawks optimization algorithm is proposed for solving re...

A new hybrid Harris hawks-Nelder-Mead optimization algorithm for solving design and manufacturing problems

There is a growing interest in designing lightweight and low-cost vehicles. In this research, the Harris hawks optimization algorithm (the HHO), the salp swarm algorithm (SSA), the grassho...

The Harris hawks optimization algorithm, salp swarm algorithm, grasshopper optimization algorithm and dragonfly algorithm for structural design optimization of vehicle components

In literature, a lot of research works have been presented on crashworthiness in order to develop crash performance of vehicles and thin-wall structures. In this research, a new hybrid optimization algorithm based on gravitational search algorithm and Nelder-Mead algorithm is introduced to improve crash performance of vehicles during frontal impact. The results show that the hybrid approach is very effective to develop crash performance of the vehicle components and thin-wall structures.

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Papers

Comparison of grey wolf, whale, water cycle, ant lion and sine-cosine algorithms for the optimization of a vehicle engine connecting rod

Moth-flame optimization algorithm to determine optimal machining parameters in manufacturing processes

A new hybrid Harris hawks-Nelder-Mead optimization algorithm for solving design and manufacturing problems

The Harris hawks optimization algorithm, salp swarm algorithm, grasshopper optimization algorithm and dragonfly algorithm for structural design optimization of vehicle components

Optimization of thin-wall structures using hybrid gravitational search and Nelder-Mead algorithm