Optimizing the multi-objective bidding strategy using min–max technique and modified water wave optimization method

Hematabadi, Ahmad Azadi; Foroud, Asghar Akbari

doi:10.1007/s00521-018-3361-0

Cited by 20 publications

(13 citation statements)

References 41 publications

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“…For order selection optimization, we propose an evolutionary algorithm based on the WWO metaheuristic [50] that takes inspiration from shallow water wave models for solving optimization problems. In particular, WWO has demonstrated superior performance on a variety of selection problems that have same or similar structure of solution space [3,8,24,41,49,53]. In WWO, each solution is analogous to a wave and is assigned with a wavelength inversely proportional to the solution fitness.…”

Section: Water Wave Optimization For Order Selectionmentioning

confidence: 99%

Hybrid evolutionary optimization for takeaway order selection and delivery path planning utilizing habit data

Zhang

et al. 2021

Complex Intell. Syst.

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The last years have seen a rapid growth of the takeaway delivery market, which has provided a lot of jobs for deliverymen. However, increasing numbers of takeaway orders and the corresponding pickup and service points have made order selection and path planning a key challenging problem to deliverymen. In this paper, we present a problem integrating order selection and delivery path planning for deliverymen, the objective of which is to maximize the revenue per unit time subject to maximum delivery path length, overdue penalty, reward/penalty for large/small number of orders, and high customer scoring reward. Particularly, we consider uncertain order ready time and customer satisfaction level, which are estimated based on historical habit data of stores and customers using a machine-learning approach. To efficiently solve this problem, we propose a hybrid evolutionary algorithm, which adapts the water wave optimization (WWO) metaheuristic to evolve solutions to the main order selection problem and employs tabu search to route the delivery path for each order selection solution. Experimental results on test instances constructed based on real food delivery application data demonstrate the performance advantages of the proposed algorithm compared to a set of popular metaheuristic optimization algorithms.

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Section: Water Wave Optimization For Order Selectionmentioning

confidence: 99%

Hybrid evolutionary optimization for takeaway order selection and delivery path planning utilizing habit data

Zhang

et al. 2021

Complex Intell. Syst.

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“…It is necessary to consider two technical aspects in order to integrate the optimization algorithms with MLP, namely, the method for encoding the agents/solutions and the procedure for determining the objective function. Although the standalone MLP models have high ability, their training algorithms may have slow convergence or may trap in local optimums [21][22][23][24][25][26][27][28][29][30][31][32][33][34]. erefore, it is essential to improve the accuracy of the MLP models.…”

Section: Optimization Algorithms For Training Mlpsmentioning

confidence: 99%

“…Water wave optimization (WWO), as an innovative optimization algorithm, has been recently used in various research fields such as the optimal reactive power dispatch, benchmark functions, and traveling salesman problem [27][28][29]. Previous research has shown that the WWO could increase the convergence speed and computation accuracy compared to PSO, GA, and other algorithms.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Daily and Monthly Reference Evapotranspiration in the Aidoghmoush Basin Using Multilayer Perceptron Coupled with Water Wave Optimization

2021

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The aim of this study is to evaluate the ability of soft computing models including multilayer perceptron- (MLP-) water wave optimization (MLP-WWO), MLP-particle swarm optimization (MLP-PSO), and MLP-genetic algorithm (MLP-GA), to simulate the daily and monthly reference evapotranspiration (ET) at the Aidoghmoush basin (Iran). Principal component analysis (PCA) was used to find the best input combination including the lagged ETs. According to the results, the ET values with 1, 2, and 3 (days) lags as well as those with 1, 2, and 3 (months) lags were the most effective variables in the formation of the PCs. The total variance proportion of inputs and eigenvalues was used to identify the most important variables. The accuracy of the models was assessed based on multiple statistical indices such as the mean absolute error (MAE), Nash–Sutcliff efficiency (NSE), and percent bias (PBIAS). The results showed that the performance of hybrid MLP models was better than that of the standalone MLP. The findings confirmed that the MLP-WWO could precisely predict ET.

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“…Therefore, there is a need to develop intelligent systems that can assist humans to make accurate judgments. Recently, water wave optimization (WWO) gained wide popularity among the research community and obtained optimal results for numerous optimization problems: (i) constrained and unconstrained optimization (Lenin et al, 2016;Manshahia, 2017;Siva et al, 2016); (ii) scheduling (Shao et al, 2018;Zhao et al, 2019a); (iii) allocation of frequency spectrum (Singh et al, 2019); (iv) multi-objective optimization (Hematabadi & Foroud, 2019;Shao et al, 2019); and (v) parameter optimization of neural network (Liu et al, 2019). The absence of global best information and premature convergence can affect the performance of the WWO algorithm with complex and discrete optimization problems.…”

Section: Illustrate the Clustering Processmentioning

confidence: 99%

Healthcare Data Analysis Using Water Wave Optimization-Based Diagnostic Model

Kumar

Kaur

2021

Journal of Information and Communication Technology

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This paper presents a new diagnostic model for various diseases. In the proposed diagnostic model, a water wave optimization (WWO) algorithm was implemented for improving the diagnosis accuracy. It was observed that the WWO algorithm suffered from the absence of global best information and premature convergence problems. Therefore in this work, some improvements were proposed to formulate the WWO algorithm as more promising and efficient. The global best information issue was addressed by using an improved solution search equation and the aim of this was to explore the global best optimal solution. Furthermore, a premature convergence problem was rectified by using a decay operator. These improvements were incorporated in the propagation and refraction phases of the WWO algorithm. The proposed algorithm was integrated into a diagnostic model for the analysis of healthcare data. The proposed algorithm aimed to improve the diagnosis accuracy of various diseases. The diverse disease datasets were considered for implementing the performance of the proposed diagnostic model based on accuracy and F-score performance indicators, while the existing techniques were regarded to compare the simulation results. The results confirmed that the WWO-based diagnostic model achieved a higher accuracy rate as compared to existing models/techniques with most disease/healthcare datasets. Therefore, it stated that the proposed diagnostic model is more promising and efficient for the diagnosis of different diseases.

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Optimizing the multi-objective bidding strategy using min–max technique and modified water wave optimization method

Cited by 20 publications

References 41 publications

Hybrid evolutionary optimization for takeaway order selection and delivery path planning utilizing habit data

Hybrid evolutionary optimization for takeaway order selection and delivery path planning utilizing habit data

Forecasting Daily and Monthly Reference Evapotranspiration in the Aidoghmoush Basin Using Multilayer Perceptron Coupled with Water Wave Optimization

Healthcare Data Analysis Using Water Wave Optimization-Based Diagnostic Model

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