A survey on new generation metaheuristic algorithms

Dökeroğlu, Tansel; Sevinç, Ender; Küçükyılmaz, Tayfun; Çoşar, Ahmet

doi:10.1016/j.cie.2019.106040

Cited by 645 publications

(269 citation statements)

References 234 publications

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“…Therefore, usage of the meta-heuristic methodologies can be alternative solution. Dokeroglu et al [47] shares an extensive survey about new generation meta-heuristics algorithms. Apart from classical methodologies, (e.g.…”

Section: Literature Reviewmentioning

confidence: 99%

Design and Analysis of Novel Hybrid Multi-Objective Optimization Approach for Data-Driven Sustainable Delivery Systems

Resat

2020

IEEE Access

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This study presents a novel two-stage solution method designed for sustainable last-mile delivery systems in urban areas. A proposed hybrid solution methodology includes multi-criteria decision-making system to select the most efficient logistics providers by considering different performance indicators, and a mixed-integer linear optimization model for last-mile cargo distributions by drones within metropolitan areas. We present a multi-objective modeling approach by considering time windows for customer services and charging operations of drones and outline important characteristics of the mathematical programming problem to minimize transportation cost (in the meantime carbon dioxide emissions) and total sustainability score of the system by using epsilon constraint method to find out the Pareto frontiers. The main novelty of the proposed solution methodology is the inclusion of many performance indicators of last-mile delivery systems into multi-objective models for design of a sustainable city logistics. Additionally, the proposed model is applied to an illustrative case by using real-life data of one of the metropolitan in Turkey. The approach is shown as comparative analysis of proposed method with other two state-of-art solution methodologies for multi-objective problems, after defining some pre-processing, symmetry breaking steps, valid inequalities, and logic cuts. INDEX TERMS Discrete optimization, mixed-integer linear programming, vehicle routing problem.

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Section: Literature Reviewmentioning

confidence: 99%

Design and Analysis of Novel Hybrid Multi-Objective Optimization Approach for Data-Driven Sustainable Delivery Systems

Resat

2020

IEEE Access

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“…Generally, ANNs are employed with EA in two main ways. Typically, most research focus on optimizing ANNs parameters, especially weights, using evolutionary techniques, especially GA [62]. These results, in better classification of the ANNs, use of the power of both methods, the learning ability of the ANNs, and the best parameter values of EA.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Evolutionary Algorithms Enhanced with Quadratic Coding and Sensing Search for Global Optimization

Hedar

Deabes

Almaraashi

et al. 2020

MCA

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Enhancing Evolutionary Algorithms (EAs) using mathematical elements significantly contribute to their development and control the randomness they are experiencing. Moreover, the automation of the primary process steps of EAs is still one of the hardest problems. Specifically, EAs still have no robust automatic termination criteria. Moreover, the highly random behavior of some evolutionary operations should be controlled, and the methods should invoke advanced learning process and elements. As follows, this research focuses on the problem of automating and controlling the search process of EAs by using sensing and mathematical mechanisms. These mechanisms can provide the search process with the needed memories and conditions to adapt to the diversification and intensification opportunities. Moreover, a new quadratic coding and quadratic search operator are invoked to increase the local search improving possibilities. The suggested quadratic search operator uses both regression and Radial Basis Function (RBF) neural network models. Two evolutionary-based methods are proposed to evaluate the performance of the suggested enhancing elements using genetic algorithms and evolution strategies. Results show that for both the regression, RBFs and quadratic techniques could help in the approximation of high-dimensional functions with the use of a few adjustable parameters for each type of function. Moreover, the automatic termination criteria could allow the search process to stop appropriately.

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“…Detailed surveys of single solution-based and population-based metaheuristics are presented (among others) in [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Metaheuristic Optimization of Power and Energy Systems: Underlying Principles and Main Issues of the ‘Rush to Heuristics’

Chicco

Mazza

2020

Energies

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In the power and energy systems area, a progressive increase of literature contributions that contain applications of metaheuristic algorithms is occurring. In many cases, these applications are merely aimed at proposing the testing of an existing metaheuristic algorithm on a specific problem, claiming that the proposed method is better than other methods that are based on weak comparisons. This ‘rush to heuristics’ does not happen in the evolutionary computation domain, where the rules for setting up rigorous comparisons are stricter but are typical of the domains of application of the metaheuristics. This paper considers the applications to power and energy systems and aims at providing a comprehensive view of the main issues that concern the use of metaheuristics for global optimization problems. A set of underlying principles that characterize the metaheuristic algorithms is presented. The customization of metaheuristic algorithms to fit the constraints of specific problems is discussed. Some weaknesses and pitfalls that are found in literature contributions are identified, and specific guidelines are provided regarding how to prepare sound contributions on the application of metaheuristic algorithms to specific problems.

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A survey on new generation metaheuristic algorithms

Cited by 645 publications

References 234 publications

Design and Analysis of Novel Hybrid Multi-Objective Optimization Approach for Data-Driven Sustainable Delivery Systems

Design and Analysis of Novel Hybrid Multi-Objective Optimization Approach for Data-Driven Sustainable Delivery Systems

Evolutionary Algorithms Enhanced with Quadratic Coding and Sensing Search for Global Optimization

Metaheuristic Optimization of Power and Energy Systems: Underlying Principles and Main Issues of the ‘Rush to Heuristics’

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