This paper proposes an effective particle swarm optimization (PSO)-based memetic algorithm (MA) for the permutation flow shop scheduling problem (PFSSP) with the objective to minimize the maximum completion time, which is a typical non-deterministic polynomial-time (NP) hard combinatorial optimization problem. In the proposed PSO-based MA (PSOMA), both PSO-based searching operators and some special local searching operators are designed to balance the exploration and exploitation abilities. In particular, the PSOMA applies the evolutionary searching mechanism of PSO, which is characterized by individual improvement, population cooperation, and competition to effectively perform exploration. On the other hand, the PSOMA utilizes several adaptive local searches to perform exploitation. First, to make PSO suitable for solving PFSSP, a ranked-order value rule based on random key representation is presented to convert the continuous position values of particles to job permutations. Second, to generate an initial swarm with certain quality and diversity, the famous Nawaz-Enscore-Ham (NEH) heuristic is incorporated into the initialization of population. Third, to balance the exploration and exploitation abilities, after the standard PSO-based searching operation, a new local search technique named NEH_1 insertion is probabilistically applied to some good particles selected by using a roulette wheel mechanism with a specified probability. Fourth, to enrich the searching behaviors and to avoid premature convergence, a simulated annealing (SA)-based local search with multiple different neighborhoods is designed and incorporated into the PSOMA. Meanwhile, an effective adaptive meta-Lamarckian learning strategy is employed to decide which neighborhood to be used in SA-based local search. Finally, to further enhance the exploitation ability, a pairwise-based local search is applied after the SA-based search. Simulation results based on benchmarks demonstrate the effectiveness of the PSOMA. Additionally, the effects of some parameters on optimization performances are also discussed

Purpose: We investigated the correlation between the number of examined lymph nodes (ELNs) and correct staging and long-term survival in non–small-cell lung cancer (NSCLC) by using large databases and determined the minimal threshold for the ELN count.Methods: Data from a Chinese multi-institutional registry and the US SEER database on stage I to IIIA resected NSCLC (2001 to 2008) were analyzed for the relationship between the ELN count and stage migration and overall survival (OS) by using multivariable models. The series of the mean positive LNs, odds ratios (ORs), and hazard ratios (HRs) were fitted with a LOWESS smoother, and the structural break points were determined by Chow test. The selected cut point was validated with the SEER 2009 cohort.Results: Although the distribution of ELN count differed between the Chinese registry (n = 5,706) and the SEER database (n = 38,806; median, 15 versus seven, respectively), both cohorts exhibited significantly proportional increases from N0 to N1 and N2 disease (SEER OR, 1.038; China OR, 1.012; both P < .001) and serial improvements in OS (N0 disease: SEER HR, 0.986; China HR, 0.981; both P < .001; N1 and N2 disease: SEER HR, 0.989; China HR, 0.984; both P < .001) as the ELN count increased after controlling for confounders. Cut point analysis showed a threshold ELN count of 16 in patients with declared node-negative disease, which were examined in the derivation cohorts (SEER 2001 to 2008 HR, 0.830; China HR, 0.738) and validated in the SEER 2009 cohort (HR, 0.837).Conclusion: A greater number of ELNs is associated with more-accurate node staging and better long-term survival of resected NSCLC. We recommend 16 ELNs as the cut point for evaluating the quality of LN examination or prognostic stratification postoperatively for patients with declared node-negative disease.

Interactive multiobjective optimization (IMO) aims at finding the most preferred solution of a decision maker with the guidance of his/her preferences which are provided progressively. During the process, the decision maker can adjust his/her preferences and explore only interested regions of the search space. In recent decades, IMO has gradually become a common interest of two distinct communities, namely, the multiple criteria decision making (MCDM) and the evolutionary multiobjective optimization (EMO). The IMO methods developed by the MCDM community usually use the mathematical programming methodology to search for a single preferred Pareto optimal solution, while those which are rooted in EMO often employ evolutionary algorithms to generate a representative set of solutions in the decision maker’s preferred region. This paper aims to give a review of IMO research from both MCDM and EMO perspectives. Taking into account four classification criteria including the interaction pattern, preference information, preference model, and search engine (i.e., optimization algorithm), a taxonomy is established to identify important IMO factors and differentiate various IMO methods. According to the taxonomy, state-of-the-art IMO methods are categorized and reviewed and the design ideas behind them are summarized. A collection of important issues, e.g., the burdens, cognitive biases and preference inconsistency of decision makers, and the performance measures and metrics for evaluating IMO methods, are highlighted and discussed. Several promising directions worthy of future research are also presented.