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:
(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.
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.
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.
Uncertainties in real-world problems impose a challenge in finding reliable solutions. If mishandled, they can lead to suboptimal or infeasible solutions. Chance constraints are a natural way to capture uncertain prob...
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ISBN:
(纸本)9783031700545;9783031700552
Uncertainties in real-world problems impose a challenge in finding reliable solutions. If mishandled, they can lead to suboptimal or infeasible solutions. Chance constraints are a natural way to capture uncertain problem parameters. They model probabilistic constraints involving the stochastic parameters and an upper bound of probability that mimics the confidence level of the solution. We focus on the knapsack problem with stochastic profits to guarantee a certain level of confidence in the profit of the solutions. We present a bi-objective fitness formulation that uses expected profit and standard deviation to capture the chance constraints. This formulation enables optimising the problem independent of a specific confidence level. We evaluate the proposed fitness formulation using well-known evolutionary algorithms GSEMO, NSGA-II and MOEA/D. Moreover, we introduce a filtering method that refines the interim populations based on the confidence levels of its solutions. We evaluate this method by applying it along with GSEMO to improve the quality of its population during optimisation. We conduct extensive experiments to show the effectiveness of these approaches using several benchmarks and present a detailed analysis of the results.
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.
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.
evolutionary design of 3D structures - an automated design by the methods of evolutionary algorithms - is a hard optimization problem. One of the contributing factors is a complex genotype-to-phenotype mapping often a...
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ISBN:
(纸本)9798400704949
evolutionary design of 3D structures - an automated design by the methods of evolutionary algorithms - is a hard optimization problem. One of the contributing factors is a complex genotype-to-phenotype mapping often associated with the genetic representations of the designs. In such case, the genetic operators may exhibit low locality, i.e., a small change introduced in a genotype may result in a significant change in the phenotype and its fitness, hampering the search process. To overcome this challenge in evolutionary design, we introduce the Distance-Targeting Mutation Operator (DTM). The aim of this operator is to create offspring whose distance to the parent solution, according to a selected dissimilarity measure, approximates a predefined value. We compare the performance of the DTM operator to the performance of the mutation operator without parent-offspring distance control in a series of evolutionary experiments. We use different genetic representations, dissimilarity measures, and optimization goals, including velocity and height of active and passive 3D structures. The introduced DTM operator outperforms the standard one in terms of best fitness in most of the considered cases.
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.
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.
A significant challenge in solving Constrained Multi-Objective Optimization Problems (CMOPs) is balancing convergence, diversity, and feasibility. Imbalance among these factors can prevent Constrained Multi-Objective ...
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ISBN:
(纸本)9789819755776;9789819755783
A significant challenge in solving Constrained Multi-Objective Optimization Problems (CMOPs) is balancing convergence, diversity, and feasibility. Imbalance among these factors can prevent Constrained Multi-Objective evolutionary algorithms (CMOEA) from converging to the Constrained Pareto Front (CPF). When dealing with problems involving complex constraints and large objective spaces, most algorithms encounter difficulties. This paper proposes a novel Competitive Swarm Optimizer (CSO) with faster convergence and stronger search capabilities. To fully utilize infeasible solutions, a two-stage Constraint Handling Technique (CHT) is introduced, which leverages well-performing infeasible solutions to help the population escape local feasibility and explore feasible regions. To promote solution diversity, weak coevolution and probabilistic coevolution methods are employed during population evolution. Additionally, continual updating of the dual-archive further enhances solution convergence and diversity. Out of 23 test suites, Proposed algorithm obtained 13 of the best HV and IGD values, far more than any other algorithm. Simulation results on the LIRCMOP and DASCMOP test suites demonstrate the superiority of the proposed algorithm over other popular algorithms.
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