A comparative review between Genetic Algorithm use in composite optimisation and the state-of-the-art in evolutionary computation

Wang, Zhenzhou; Sobey, Adam

doi:10.1016/j.compstruct.2019.111739

Cited by 116 publications

(54 citation statements)

References 159 publications

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“…Once all the offspring have been generated or when the second generation is ready, the chromosomes are evaluated again using the objective function and the cycle is repeated, as shown in Figures 3a and 4. References [14][15][16] provide recent advances in this area.…”

Section: Genetic Algorithmmentioning

confidence: 99%

Non-Deterministic Methods and Surrogates in the Design of Rockfill Dams

Das

Soulaïmani

2021

Applied Sciences

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The parameters of the constitutive models used in the design of rockfill dams are associated with a high degree of uncertainty. This occurs because rockfill dams are comprised of numerous zones, each with different soil materials, and it is not feasible to extract materials from such structures to accurately ascertain their behavior or their respective parameters. The general approach involves laboratory tests using small material samples or empirical data from the literature. However, such measures lack an accurate representation of the actual scenario, resulting in uncertainties. This limits the suitability of the model in the design process. Inverse analysis provides an option to better understand dam behavior. This procedure involves the use of real monitored data, such as deformations and stresses, from the dam structure via installed instruments. Fundamentally, it is a non-destructive approach that considers optimization methods and actual performance data to determine the values of the parameters by minimizing the differences between simulated and observed results. This paper considers data from an actual rockfill dam and proposes a surrogate assisted non-deterministic framework for its inverse analysis. A suitable error/objective function that measures the differences between the actual and simulated displacement values is defined first. Non-deterministic algorithms are used as the optimization technique, as they can avoid local optima and are more robust when compared to the conventional deterministic methods. Three such approaches, the genetic algorithm, differential evolution, and particle swarm optimization are evaluated to identify the best strategy in solving problems of this nature. A surrogate model in the form of a polynomial regression is studied and recommended in place of the actual numerical model of the dam to reduce computation cost. Finally, this paper presents the relevant dam parameters estimated by the analysis and provides insights into the performance of the three procedures to solve the inverse problem.

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Section: Genetic Algorithmmentioning

confidence: 99%

Non-Deterministic Methods and Surrogates in the Design of Rockfill Dams

Das

Soulaïmani

2021

Applied Sciences

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“…Genetic algorithms (GAs) [23] are a subset of evolutionary algorithms [24], that have emerged as flexible and efficient metaheuristic methods for solving optimization problems and achieving a high level of problem-solving efficacy in most research domains, e.g., aircraft design [25], battery systems [26], resource allocation [27], job-shop scheduling [28], virtual machine placement [29], cloud task scheduling [30], quadratic assignment [31], and vehicle…”

Section: Genetic Algorithmsmentioning

confidence: 99%

A Novel Many-Objective Recommendation Algorithm for Multistakeholders

Wang

Chen

2020

IEEE Access

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Most traditional recommender systems focus specifically on increasing consumer satisfaction by providing a list of relevant content to consumers. However, the perspectives of other multisided marketplace stakeholders are also equally important, i.e., the exposure for suppliers or providers and profit for the platform. The suppliers want their products to be presented to users, and the objective of the platform is to maximize their profit. Nevertheless, because consumers' preferences and the objectives of providers as well as the platform may conflict with each other, it degrades the utility of the recommendation methods by only considering users' views. Therefore, in this work, we use a many-objective optimization method to maintain a tradeoff among five objectives for three stakeholders and obtain multiple Pareto front solutions in a single run. We first combine customer lifetime value and user purchase preference to create a new similarity model (Sim_RFMP) to increase the recommendation accuracy of the recommendation list. Furthermore, we propose a many-objective model (NBHXMAOEA) for multistakeholder recommendation. In NBHXMAOEA, we present a novel N-block heuristic crossover operator (NBHX) that recombines blocks of chromosomes based on heuristics. Through extensive experiments, the results demonstrate that our proposed NBHXMAOEA achieves superior performance in terms of average accuracy, diversity, novelty, provider coverage, and platform profit to its competing methods. INDEX TERMS Many-objective, recommender systems, similarity model, stakeholders.

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“…The schematic diagram of the non-linear system of coupled two-tanks is shown in Fig. 5 and its first principles model is shown in (18), (19).…”

Section: Example 1: Non-linear Coupled Two-tank Systemmentioning

confidence: 99%

“…where: The system actuators present the constraints given by (20). The parameters of the proposed model in (18), (19) are shown in Table 1.…”

Section: Example 1: Non-linear Coupled Two-tank Systemmentioning

confidence: 99%

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A Loop Pairing Method for Non-Linear Multivariable Control Systems Under a Multi-Objective Optimization Approach

et al. 2020

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In the multivariable control literature there are few techniques that face the problem of selecting suitable loop pairings in non-linear multivariable systems. Most techniques analyze the linearized system at a specific operating point. This paper proposes a new methodology to optimally and simultaneously select the loop pairings and the tuning of the parameters of the decentralized control by applying a multi-objective optimization approach directly on the non-linear system. The main contribution of this work is that the proposed methodology enables a detailed multi-dimensional analysis of the performances and trade-offs in the available loop pairings to control a multivariable non-linear system. The methodology is applied in this paper to three examples that analyze how the different types of loop pairings conflict. In one of the examples, the proposed methodology was applied first in the linearized system and later in the non-linear system. The results were contradictory and show how the application of loop pairing techniques for linear systems can be inaccurate when they are applied on a non-linear system previously linearized at an operating point. The following examples show that the operating point of a non-linear system, the design objectives of each multiobjective problem, as well as the designer's preferences have important roles in the selection of an optimal loop pairing. INDEX TERMS Multivariable control system, non-linear systems, loop pairing, decentralized control structures, multi-objective evolutionary optimization, Pareto front.

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A comparative review between Genetic Algorithm use in composite optimisation and the state-of-the-art in evolutionary computation

Cited by 116 publications

References 159 publications

Non-Deterministic Methods and Surrogates in the Design of Rockfill Dams

Non-Deterministic Methods and Surrogates in the Design of Rockfill Dams

A Novel Many-Objective Recommendation Algorithm for Multistakeholders

A Loop Pairing Method for Non-Linear Multivariable Control Systems Under a Multi-Objective Optimization Approach

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