An Analysis of the Inertia Weight Parameter for Binary Particle Swarm Optimization

In particle swarm optimization (PSO), the inertia weight is an important parameter for controlling its search capability. There have been intensive studies of the inertia weight in continuous optimization, but little attention has been paid to the binary case. This paper comprehensively investigates the effect of the inertia weight on the performance of binary PSO (BPSO), from both theoretical and empirical perspectives. A mathematical model is proposed to analyze the behavior of BPSO, based on which several lemmas and theorems on the effect of the inertia weight are derived. Our research findings suggest that in the binary case, a smaller inertia weight enhances the exploration capability while a larger inertia weight encourages exploitation. Consequently, this paper proposes a new adaptive inertia weight scheme for BPSO. This scheme allows the search process to start first with exploration and gradually move toward exploitation by linearly increasing the inertia weight. The experimental results on 0/1 knapsack problems show that the BPSO with the new increasing inertia weight scheme performs significantly better than that with the conventional decreasing and constant inertia weight schemes. This paper verifies the efficacy of increasing inertia weight in BPSO. © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Learning Traffic Signal Control via Genetic Programming

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Explaining Genetic Programming-Evolved Routing Policies for Uncertain Capacitated Arc Routing Problems

—Genetic programming (GP) has been successfully used to evolve routing policies that can make real-time routing decisions for uncertain arc routing problems. Although the evolved routing policies are highly effective, they are typically very large and complex, and hard to be understood and trusted by real users. Existing studies have attempted to improve the interpretability by developing new GP approaches to evolve both effective and interpretable (e.g., with smaller program size) routing policies. However, they still have limitations due to the tradeoff between effectiveness and interpretability. To address this issue, we propose a new post-hoc explanation approach to explaining the effective but complex routing policies evolved by GP. The new approach includes a local ranking explanation and a global explanation module. The local ranking explanation uses particle swarm optimization to learn an interpretable linear model that accurately explains the local behavior of the routing policy for each decision situation. Then, the global explanation module uses a clustering technique to summarize the local explanations into a global explanation. The experimental results and case studies on the benchmark datasets show that the proposed method can obtain accurate and understandable explanations of the routing policies evolved for uncertain arc routing problems. Our explanation approach is not restricted to uncertain arc routing, but has a great potential to be generalized to other optimization and machine learning problems, such as learning classifier systems and reinforcement learning

A Multi-Objective Genetic Programming Algorithm With α Dominance and Archive for Uncertain Capacitated Arc Routing Problem

The uncertain capacitated arc routing problem (UCARP) is an important combinatorial optimization problem with many applications in the real world. Genetic programming hyper-heuristic has been successfully used to automatically evolve routing policies, which can make real-time routing decisions for UCARPs. It is desired to evolve routing policies that are both effective and small/simple to be easily understood. The effectiveness and size are two potentially conflicting objectives. A further challenge is the objective selection bias issue, i.e., it is much more likely to obtain small but ineffective routing policies than the effective ones that are typically large. In this article, we propose a new multiobjective genetic programming algorithm to evolve effective and small routing policies. The new algorithm employs the α dominance strategy with a newly proposed α adaptation scheme to address the objective selection bias issue. In addition, it contains a new archive strategy to prevent the loss of promising individuals due to the rotation of training instances. The experimental results showed that the newly proposed algorithm can evolve significantly better routing policies than the current state-of-the-art algorithms for UCARP in terms of both effectiveness and size. We have also analyzed the evolved routing policies to show better interpretability

Feature selection in evolving job shop dispatching rules with genetic programming

Genetic Programming (GP) has been successfully used to automatically design dispatching rules in job shop scheduling. The goal of GP is to evolve a priority function that will be used to order the waiting jobs at each decision point, and decide the next job to be processed. To this end, the proper terminals (i.e. job shop features) have to be decided. When evolving the priority function, various job shop features can be included in the terminal set. However, not all the features are helpful, and some features are irrelevant to the rule. Including irrelevant features into the terminal set enlarges the search space, and makes it harder to achieve promising areas. Thus, it is important to identify the important features and remove the irrelevant ones to improve the GP-evolved rules. This paper proposes a domain-knowledge-free feature ranking and selection approach. As a result, the terminal set is significantly reduced and only the most important features are selected. The experimental results show that using only the selected features can lead to significantly better GP-evolved rules on both training and unseen test instances. © Mei 2016. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in 'GECCO '16: Proceedings of the Genetic and Evolutionary Computation Conference', https://doi.org/10.1145/2908812.2908822

Confidence-based Ant Colony Optimization for Capacitated Electric Vehicle Routing Problem with Comparison of Different Encoding Schemes

The blossoming of electric vehicles gives rise to a new vehicle routing problem called capacitated electric vehicle routing problem. Since charging is not as convenient as refueling, both the service of customers and the recharging of vehicles should be considered. In this paper, we propose a confidence-based bi-level ant colony optimization algorithm to solve the problem. It divides the whole problem into the upper-level sub-problem capacitated vehicle routing problem and the lower-level sub-problem fixed routing vehicle charging problem. For the upper-level sub-problem, an ant colony optimization algorithm is used to generate customer service sequence. Both the direct encoding scheme and the order-first split-second encoding scheme are implemented to make a guideline of their applicable scenes. For the lower-level sub-problem, a new heuristic called simple enumeration is proposed to generate recharging schedules for vehicles. Between the two sub-problems, a confidence-based selection method is proposed to select promising customer service sequence to conduct local search and lower-level optimization. By setting adaptive confidence thresholds, the inferior service sequences that have little chance to become the iteration best are eliminated during the execution. Experiments show that the proposed algorithm has reached the state-of-the-art level and updated eight best known solutions of the benchmark

Learning from Offline and Online Experiences: A Hybrid Adaptive Operator Selection Framework

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Genetic programming for production scheduling: a survey with a unified framework

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Learning Penalisation Criterion of Guided Local Search for Large Scale Vehicle Routing Problem

A recent case of success, the Knowledge-Guided Local Search was able to efficiently and effectively solve several (Large-Scale) Vehicle Routing Problems. This method presents an interesting concept of route compactness in their search process and uses it to penalise the solutions instead of using the traditional distance measure. Although mostly being successful, this measure sometimes leads to underperforming solutions when compared to the distance aspect. Based on the assumption that the best Guide Local Search penalisation criterion depends on the VRP instance, we make an analysis on how the algorithm behaves across different instances and also propose a Machine Learning model to learn to predict the best penalty criterion for a given instance. Genetic Programming, Support-Vector Machines and Random Forests are used in this classification task. Additionally, we also consider a regression model in order to estimate the improvement given for each mode. Results show that it is possible to find the correct class using the selected features and, in fact, some models were able to classify the majority of instances correctly. However, this is not consistent across different instances

A novel methodology for architectural wind environment study by integrating CFD simulation, multiple parametric tools and evaluation criteria

Nowadays wind environment research usually requires lots of comparisons of simulations to study influences on wind environment of building shapes and arrangements. This research aims to develop a novel methodology by integrating computational fluid dynamics (CFD) simulation, parametric tools and evaluation criteria. The integration of multiple tools can provide abundant functions and an efficient modelling-simulation-analysis solution for comparison studies. The methodology is consisted of parametric design, CFD simulation method and analysis method. It is further demonstrated in the case study of square form and scattered configuration to study the relationship between influences on winds and building variables through iterative analysis. For square form, the increase of edge length increases the influence, because more winds are obstructed by larger windward surfaces; the increase of rotation angle reduces the influence, because it is easier for winds to flow around non-vertical windward surfaces. For scattered configuration, the increase of building intervals reduces the influences on winds, because it is easier for winds to flow through larger intervals. In summary, the novel methodology provides an accurate and efficient integrated solution for wind environmental studies of contemporary buildings to explore basic laws for architects to improve their design on the early stage