The best structure of multicomponent separation techniques can be obtained using optimal distillation sequencing. Because distillation sequences contribute significantly to the fixed and operational cost of the entire...
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The best structure of multicomponent separation techniques can be obtained using optimal distillation sequencing. Because distillation sequences contribute significantly to the fixed and operational cost of the entire chemical process, developing a systematic approach for choosing the most appropriate and economic distillation sequences becomes an important field of study. Due to its high dimensional space and combinatorial nature, synthesis of the optimal conventional distillation column sequence is a tough problem in the field of process plant development and optimization. A novel method for the synthesis of an optimal conventional distillation column sequence is suggested in this study. Genetic algorithm, an evolutionary algorithm is at the heart of the proposed method. The Total Annual Cost (TAC) is the main basis used to evaluate alternative configurations. To estimate the total cost of each sequence, rigorous methods are used to design all columns in the sequence. The proposed method's performance and that of the conventional quantitative approach are compared using the results of a five component benchmark test problem used by researchers in this field. According to the comparison results, the suggested algorithm outclasses the other methods and is more adaptable than other existing approaches.
Scheduling problems require evolutionary methods, but they often struggle with complexity. To enhance solutions, heuristic knowledge can be integrated into fitness functions, although this may introduce bias towards l...
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ISBN:
(数字)9783031611377
ISBN:
(纸本)9783031611360;9783031611377
Scheduling problems require evolutionary methods, but they often struggle with complexity. To enhance solutions, heuristic knowledge can be integrated into fitness functions, although this may introduce bias towards local minima. This paper proposes a cooperative multi-fitness approach that combines genetic diversity with heuristic solutions to support a standard fitness function. Lamarckism can assist in the reconstruction of chromosomes, for direct evaluation by the standard fitness decoder. This combination of genetic diversity and heuristic knowledge aims to achieve superior solutions. This evaluation approach is applied to a genetic algorithm for scientific workflow scheduling, minimizing total execution time in cloud computing.
Robotic surgery makes use of autonomous robots that can perform some surgical tasks on their own. Surgical robots performed well in conjunction with machine learning, particularly reinforcement learning (RL), allowing...
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Robotic surgery makes use of autonomous robots that can perform some surgical tasks on their own. Surgical robots performed well in conjunction with machine learning, particularly reinforcement learning (RL), allowing them to be used in complex environments, such as cutting a pre-determined pattern on soft tissue with surgical scissors and gripper. There is no doubt that soft tissue is deformable, so using a tensioning policy can determine appropriate tension direction from the pinch point at any time to have an accurate cut in the pre-determined trajectory. In this study, we used the deep reinforcement learning (DRL) approach to find an optimal tensioning policy for cutting soft tissues. In addition, we used an evolutionary algorithm with the operators appropriate to the problem and the learned tensioning policy to find the sequence of tensioning actions. The objective of this study is to determine the optimal tensioning policy and the best tensioning action sequence. The experimental results show that using the learned policy results in smaller damage and error and lead to the highest scores compared to the previous studies.
Cloud computing has revolutionized the provisioning and access of computing resources, offering scalable and flexible alternatives to traditional infrastructure. However, defining how to use these computational resour...
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ISBN:
(纸本)9783031790317;9783031790324
Cloud computing has revolutionized the provisioning and access of computing resources, offering scalable and flexible alternatives to traditional infrastructure. However, defining how to use these computational resources may be challenging. This paper addresses the challenge of workflow scheduling in cloud environments, focusing on Amazon Web Services (AWS) Elastic Compute Cloud (EC2). We present HEACT, a novel approach that integrates a multi-objective evolutionary algorithm with a specialist scheduling heuristic. The evolutionary algorithm is responsible for generating an initial set of machines (with their performance capability and cost information). The set is sent to the specialist scheduling heuristic for efficient task assignment in these machines. Our approach considers fourteen AWS regions, accurate pricing information from AWS, and employs SimGrid to simulate task execution. The proposed method was benchmarked considering established heuristics (HEFT, PEFT, HSIP, MPEFT) and meta-heuristics (NSGA-II, AGEMOEA2). Results demonstrated that the combinations of AGEMOEA2 with MPEFT and AGEMOEA2 with HEFT yield the best performance, indicating AGEMOEA2's efficacy as a state-of-the-art meta-heuristic for workflow scheduling.
How to handle the high dimensional search space effectively is a burning research question in evolutionary computation when solving large-scale multiobjective optimization problems. To alleviate it, we propose a new e...
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ISBN:
(纸本)9798350377859;9798350377842
How to handle the high dimensional search space effectively is a burning research question in evolutionary computation when solving large-scale multiobjective optimization problems. To alleviate it, we propose a new evolutionary algorithm with a tactic of innovization-based representation learning, which aims to discover innovative solution principles from compressed representations. Specifically, a neural net, including an encoder and a decoder, is iteratively learned to transform input solutions into the predictions that dominate them. The encoder thus can capture their compressed representations that retain the information necessary for making good predictions. The decoder can create innovative promising solutions from newly explored representations by evolutionary search. Finally, the proposed algorithm is tested on synthetic and real-world benchmarks with the dimension of variables up to 10(4). Comparison results show the superior performance of the proposed optimizer in solving these benchmarks within 10(5) function evaluations.
Solving constrained multi-objective optimization problems with evolutionary algorithms has attracted considerable *** constrained multi-objective optimization evolutionary algorithms(CMOEAs)have been developed with th...
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Solving constrained multi-objective optimization problems with evolutionary algorithms has attracted considerable *** constrained multi-objective optimization evolutionary algorithms(CMOEAs)have been developed with the use of different algorithmic strategies,evolutionary operators,and constraint-handling *** performance of CMOEAs may be heavily dependent on the operators used,however,it is usually difficult to select suitable operators for the problem at ***,improving operator selection is promising and necessary for *** work proposes an online operator selection framework assisted by Deep Reinforcement *** dynamics of the population,including convergence,diversity,and feasibility,are regarded as the state;the candidate operators are considered as actions;and the improvement of the population state is treated as the *** using a Q-network to learn a policy to estimate the Q-values of all actions,the proposed approach can adaptively select an operator that maximizes the improvement of the population according to the current state and thereby improve the algorithmic *** framework is embedded into four popular CMOEAs and assessed on 42 benchmark *** experimental results reveal that the proposed Deep Reinforcement Learning-assisted operator selection significantly improves the performance of these CMOEAs and the resulting algorithm obtains better versatility compared to nine state-of-the-art CMOEAs.
In recent years, a large number of approaches to constrained multi-objective optimization problems(CMOPs) have been proposed, focusing on developing tweaked strategies and techniques for handling constraints. However,...
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In recent years, a large number of approaches to constrained multi-objective optimization problems(CMOPs) have been proposed, focusing on developing tweaked strategies and techniques for handling constraints. However, an overly finetuned strategy or technique might overfit some problem types,resulting in a lack of versatility. In this article, we propose a generic search strategy that performs an even search in a promising region. The promising region, determined by obtained feasible non-dominated solutions, possesses two general ***, the constrained Pareto front(CPF) is included in the promising region. Second, as the number of feasible solutions increases or the convergence performance(i.e., approximation to the CPF) of these solutions improves, the promising region shrinks. Then we develop a new strategy named even search,which utilizes the non-dominated solutions to accelerate convergence and escape from local optima, and the feasible solutions under a constraint relaxation condition to exploit and detect feasible regions. Finally, a diversity measure is adopted to make sure that the individuals in the population evenly cover the valuable areas in the promising region. Experimental results on 45 instances from four benchmark test suites and 14 real-world CMOPs have demonstrated that searching evenly in the promising region can achieve competitive performance and excellent versatility compared to 11 most state-of-the-art methods tailored for CMOPs.
This study proposes to generalize the hybridization of evolutionary algorithm for solving large dimensional continuous global optimization problems. Inspired by various dual hybridizations being used, this paper propo...
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ISBN:
(纸本)9781479939756
This study proposes to generalize the hybridization of evolutionary algorithm for solving large dimensional continuous global optimization problems. Inspired by various dual hybridizations being used, this paper proposes hybrid evolutionary algorithms based on crossing over the FFA, PSO, BAT, ACO and GA algorithms. The main idea of the proposed method is to integrate the aforementioned algorithms by following best solutions of other algorithm using roulette wheel approach. The aim of the proposed hybrid algorithm was to enable problem solving using two or more evolutionary algorithms as is, without modification, besides effectively exploring and exploiting of the problem search space. Simulations for a series of benchmark test functions justify that an adroit hybridization of various evolutionary algorithms could yield a robust and efficient means of solving wide range of global optimization problems than the standalone evolutionary algorithms.
Hypervolume indicator is one of the most classic and commonly used metrics in the field of multi-objective optimization. It is widely used to solve multi-objective optimization problems. However, as the number of obje...
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ISBN:
(纸本)9798350377859;9798350377842
Hypervolume indicator is one of the most classic and commonly used metrics in the field of multi-objective optimization. It is widely used to solve multi-objective optimization problems. However, as the number of objectives increases, the computational time for calculating the hypervolume contribution increases sharply. This paper introduces a simple and computationally efficient method for hypervolume contribution, referred to as pointwise Hypervolume Contribution (pHVC). This approach retains the beneficial properties of hypervolume and solves the curse of dimensionality of the hpyervolume indicator. We apply pHVC to evolutionary multi-objective optimization algorithm and propose the pHVC-EMOA. Experimental results demonstrate that pHVC-EMOA is more efficient than the other hypervolume-based EMOAs.
Developing optical systems, particularly those consisting of spherical lenses, is relevant for various applications such as lithographic scanners and metrology equipment. The design process of an optical system typica...
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ISBN:
(纸本)9781510673656;9781510673649
Developing optical systems, particularly those consisting of spherical lenses, is relevant for various applications such as lithographic scanners and metrology equipment. The design process of an optical system typically involves the optimization of specific objectives to ensure the best performance. As a common example of such an objective, we consider the problem of determining the lens curvatures that result in a sufficiently small root mean square (RMS) spot size. Optimization algorithms are commonly employed to solve this problem by heuristically eliminating sub-optimal optical designs. This class of algorithms includes the damped least squares (DLS) widely applied in commercial software and advanced methods like Saddle Point Construction. However, within a restricted computational budget, these optimizers are limited in exploring potentially promising novel solutions since they heavily rely on the initial specific designs that must conform to complex or unknown requirements. In this work, we address the considered problem with a modified Hill-Valley evolutionary Algorithm (HillVallEA), which proved itself as one of the best state-of-the-art metaheuristics for multimodal black-box optimization. We demonstrate that our algorithm locates a diverse set of high-quality optical designs with four lenses in a single run even when initialized with random starting curvatures. This is the first result in this domain when an optimization algorithm that does not take specific optical properties into account can still generate relevant and high-performing optical systems. Furthermore, we show the benefits of the proposed methodology for the diversity of the obtained set of solutions, while maintaining a solution of the same quality as the one found by the most prominent algorithm in the domain. We provide analyses of the obtained solutions according to: 1) tolerance to the alignment of lenses, 2) susceptibility to small variations of lens curvatures.
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