In pedagogy, teachers usually separate mixed-level students into different levels, treat them differently and teach them in accordance with their cognitive and learning abilities. Inspired from this ...idea, we consider particles in the swarm as mixed-level students and propose a level-based learning swarm optimizer (LLSO) to settle large-scale optimization, which is still considerably challenging in evolutionary computation. At first, a level-based learning strategy is introduced, which separates particles into a number of levels according to their fitness values and treats particles in different levels differently. Then, a new exemplar selection strategy is designed to randomly select two predominant particles from two different higher levels in the current swarm to guide the learning of particles. The cooperation between these two strategies could afford great diversity enhancement for the optimizer. Further, the exploration and exploitation abilities of the optimizer are analyzed both theoretically and empirically in comparison with two popular particle swarm optimizers. Extensive comparisons with several state-of-the-art algorithms on two widely used sets of large-scale benchmark functions confirm the competitive performance of the proposed optimizer in both solution quality and computational efficiency. Finally, comparison experiments on problems with dimensionality increasing from 200 to 2000 further substantiate the good scalability of the developed optimizer.
The increasing popularity of battery-limited electric vehicles puts forward an important issue of how to charge the vehicles effectively. This problem, commonly referred to as Electric Vehicle ...Charging Scheduling (EVCS), has been proven to be NP-hard. Most of the existing works formulate the EVCS problem simply as a constrained shortest path finding problem and treat it by discrete optimization. However, other variables such as the charging amount of energy and the charging option at a station need to be considered in practical use. This paper hence formulates the EVCS problem as a hierarchical mixed-variable optimization problem, considering the dependency among the station selection, the charging option at each station and the charging amount settings. To adapt to the new problem model, we specifically design a Mixed-Variable Differentiate Evolution (MVDE) as the scheduling algorithm for our proposed EVCS system. The MVDE contains several specific operators, including a charging station route construction, a hierarchical mixed-variable mutation operator and a constraint-aware evaluation operator. Experimental results validate the effectiveness of our proposed MVDE-based system on both synthetic and real-world transportation networks.
Seeking multiple optima simultaneously, which multimodal optimization aims at, has attracted increasing attention but remains challenging. Taking advantage of ant colony optimization (ACO) algorithms ...in preserving high diversity, this paper intends to extend ACO algorithms to deal with multimodal optimization. First, combined with current niching methods, an adaptive multimodal continuous ACO algorithm is introduced. In this algorithm, an adaptive parameter adjustment is developed, which takes the difference among niches into consideration. Second, to accelerate convergence, a differential evolution mutation operator is alternatively utilized to build base vectors for ants to construct new solutions. Then, to enhance the exploitation, a local search scheme based on Gaussian distribution is self-adaptively performed around the seeds of niches. Together, the proposed algorithm affords a good balance between exploration and exploitation. Extensive experiments on 20 widely used benchmark multimodal functions are conducted to investigate the influence of each algorithmic component and results are compared with several state-of-the-art multimodal algorithms and winners of competitions on multimodal optimization. These comparisons demonstrate the competitive efficiency and effectiveness of the proposed algorithm, especially in dealing with complex problems with high numbers of local optima.
3D integration with through-silicon via (TSV) is a promising candidate to perform system-level integration with smaller package size, higher interconnection density, and better performance. TSV ...fabrication is the key technology to permit communications between various strata of the 3D integration system. TSV fabrication steps, such as etching, isolation, metallization processes, and related failure modes, as well as other characterizations are discussed in this invited review paper.
Research into developing effective computer aided techniques for planning software projects is important and challenging for software engineering. Different from projects in other fields, software ...projects are people-intensive activities and their related resources are mainly human resources. Thus, an adequate model for software project planning has to deal with not only the problem of project task scheduling but also the problem of human resource allocation. But as both of these two problems are difficult, existing models either suffer from a very large search space or have to restrict the flexibility of human resource allocation to simplify the model. To develop a flexible and effective model for software project planning, this paper develops a novel approach with an event-based scheduler (EBS) and an ant colony optimization (ACO) algorithm. The proposed approach represents a plan by a task list and a planned employee allocation matrix. In this way, both the issues of task scheduling and employee allocation can be taken into account. In the EBS, the beginning time of the project, the time when resources are released from finished tasks, and the time when employees join or leave the project are regarded as events. The basic idea of the EBS is to adjust the allocation of employees at events and keep the allocation unchanged at nonevents. With this strategy, the proposed method enables the modeling of resource conflict and task preemption and preserves the flexibility in human resource allocation. To solve the planning problem, an ACO algorithm is further designed. Experimental results on 83 instances demonstrate that the proposed method is very promising.
To assess the benefits of epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors as neoadjuvant/adjuvant therapies in locally advanced
mutation-positive non-small-cell lung cancer.
This ...was a multicenter (17 centers in China), open-label, phase II, randomized controlled trial of erlotinib versus gemcitabine plus cisplatin (GC chemotherapy) as neoadjuvant/adjuvant therapy in patients with stage IIIA-N2 non-small-cell lung cancer with
mutations in exon 19 or 21 (EMERGING). Patients received erlotinib 150 mg/d (neoadjuvant therapy, 42 days; adjuvant therapy, up to 12 months) or gemcitabine 1,250 mg/m
plus cisplatin 75 mg/m
(neoadjuvant therapy, two cycles; adjuvant therapy, up to two cycles). Assessments were performed at 6 weeks and every 3 months postsurgery. The primary end point was objective response rate (ORR) by Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1; secondary end points were pathologic complete response, progression-free survival (PFS), overall survival, safety, and tolerability.
Of 386 patients screened, 72 were randomly assigned to treatment (intention-to-treat population), and 71 were included in the safety analysis (one patient withdrew before treatment). The ORR for neoadjuvant erlotinib versus GC chemotherapy was 54.1% versus 34.3% (odds ratio, 2.26; 95% CI, 0.87 to 5.84;
= .092). No pathologic complete response was identified in either arm. Three (9.7%) of 31 patients and zero of 23 patients in the erlotinib and GC chemotherapy arms, respectively, had a major pathologic response. Median PFS was significantly longer with erlotinib (21.5 months) versus GC chemotherapy (11.4 months; hazard ratio, 0.39; 95% CI, 0.23 to 0.67;
< .001). Observed adverse events reflected those most commonly seen with the two treatments.
The primary end point of ORR with 42 days of neoadjuvant erlotinib was not met, but the secondary end point PFS was significantly improved.
Detecting community structure has become one important technique for studying complex networks. Although many community detection algorithms have been proposed, most of them focus on separated ...communities, where each node can belong to only one community. However, in many real-world networks, communities are often overlapped with each other. Developing overlapping community detection algorithms thus becomes necessary. Along this avenue, this paper proposes a maximal clique based multiobjective evolutionary algorithm (MOEA) for overlapping community detection. In this algorithm, a new representation scheme based on the introduced maximal-clique graph is presented. Since the maximal-clique graph is defined by using a set of maximal cliques of original graph as nodes and two maximal cliques are allowed to share the same nodes of the original graph, overlap is an intrinsic property of the maximal-clique graph. Attributing to this property, the new representation scheme allows MOEAs to handle the overlapping community detection problem in a way similar to that of the separated community detection, such that the optimization problems are simplified. As a result, the proposed algorithm could detect overlapping community structure with higher partition accuracy and lower computational cost when compared with the existing ones. The experiments on both synthetic and real-world networks validate the effectiveness and efficiency of the proposed algorithm.
Insurance has been increasingly realized as an important way of investment and risk aversion. Fruitful insurance products are launched by insurers, but there is little research on how to make a ...proper insurance investment plan for a specific policyholder given different kinds of policies. In this paper, we aim to propose a practical approach to multipolicy insurance investment planning with a data-driven model and an estimation of distribution algorithm (EDA). First, by making use of the insurance data accumulated in the modern financial market, an optimization model about how to choose endowment and hospitalization policies is built to maximize the yearly profit of insurance investment. With the model parameters set according to the real data from insurance market, the resulting plan is practical and individualized. Second, as the optimal solution cannot be achieved by mathematical deduction under this data-driven model, an EDA is introduced. To adapt the EDA for the considered problem, the proposed EDA is mixed with both the continuous and discrete probability distribution models to handle different kinds of variables. In addition, an adaptive scheme for choosing suitable distribution models and an efficient constraint handling strategy are proposed. Experiments under different conditions confirm the effectiveness and efficiency of the proposed model and method.
ADJUVANT-CTONG1104 (ClinicalTrials.gov identifier: NCT01405079), a randomized phase III trial, showed that adjuvant gefitinib treatment significantly improved disease-free survival (DFS) versus ...vinorelbine plus cisplatin (VP) in patients with epidermal growth factor receptor (
) mutation-positive resected stage II-IIIA (N1-N2) non-small-cell lung cancer (NSCLC). Here, we report the final overall survival (OS) results.
From September 2011 to April 2014, 222 patients from 27 sites were randomly assigned 1:1 to adjuvant gefitinib (n = 111) or VP (n = 111). Patients with resected stage II-IIIA (N1-N2) NSCLC and
-activating mutation were enrolled, receiving gefitinib for 24 months or VP every 3 weeks for four cycles. The primary end point was DFS (intention-to-treat ITT population). Secondary end points included OS, 3-, 5-year (y) DFS rates, and 5-year OS rate. Post hoc analysis was conducted for subsequent therapy data.
Median follow-up was 80.0 months. Median OS (ITT) was 75.5 and 62.8 months with gefitinib and VP, respectively (hazard ratio HR, 0.92; 95% CI, 0.62 to 1.36;
= .674); respective 5-year OS rates were 53.2% and 51.2% (
= .784). Subsequent therapy was administered upon progression in 68.4% and 73.6% of patients receiving gefitinib and VP, respectively. Subsequent targeted therapy contributed most to OS (HR, 0.23; 95% CI, 0.14 to 0.38) compared with no subsequent therapy. Updated 3y DFS rates were 39.6% and 32. 5% with gefitinib and VP (
= .316) and 5y DFS rates were 22. 6% and 23.2% (
= .928), respectively.
Adjuvant therapy with gefitinib in patients with early-stage NSCLC and
mutation demonstrated improved DFS over standard of care chemotherapy. Although this DFS advantage did not translate to a significant OS difference, OS with adjuvant gefitinib was one of the longest observed in this patient group compared with historic data.
In nature, almost every organism ages and has a limited lifespan. Aging has been explored by biologists to be an important mechanism for maintaining diversity. In a social animal colony, aging makes ...the old leader of the colony become weak, providing opportunities for the other individuals to challenge the leadership position. Inspired by this natural phenomenon, this paper transplants the aging mechanism to particle swarm optimization (PSO) and proposes a PSO with an aging leader and challengers (ALC-PSO). ALC-PSO is designed to overcome the problem of premature convergence without significantly impairing the fast-converging feature of PSO. It is characterized by assigning the leader of the swarm with a growing age and a lifespan, and allowing the other individuals to challenge the leadership when the leader becomes aged. The lifespan of the leader is adaptively tuned according to the leader's leading power. If a leader shows strong leading power, it lives longer to attract the swarm toward better positions. Otherwise, if a leader fails to improve the swarm and gets old, new particles emerge to challenge and claim the leadership, which brings in diversity. In this way, the concept "aging" in ALC-PSO actually serves as a challenging mechanism for promoting a suitable leader to lead the swarm. The algorithm is experimentally validated on 17 benchmark functions. Its high performance is confirmed by comparing with eight popular PSO variants.
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