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Constrained multiobjective optimization problems (CMOPs) involve both conflicting objective functions and various constraints. Due to the presence of constraints, CMOPs' Pareto-optimal solutions are very likely lying on constraint boundaries. The experience from the constrained single-objective optimization has shown that to quickly obtain such an optimal solution, the search should surround the boundary of the feasible region from both the feasible and infeasible sides. In this article, we extend this idea to cope with CMOPs and, accordingly, we propose a novel constrained multiobjective evolutionary algorithm with bidirectional coevolution, called BiCo. BiCo maintains two populations, that is: 1) the main population and 2) the archive population. To update the main population, the constraint-domination principle is equipped with an NSGA-II variant to move the population into the feasible region and then to guide the population toward the Pareto front (PF) from the feasible side of the search space. While for updating the archive population, a nondominated sorting procedure and an angle-based selection scheme are conducted in sequence to drive the population toward the PF within the infeasible region while maintaining good diversity. As a result, BiCo can get close to the PF from two complementary directions. In addition, to coordinate the interaction between the main and archive populations, in BiCo, a restricted mating selection mechanism is developed to choose appropriate mating parents. Comprehensive experiments have been conducted on three sets of CMOP benchmark functions and six real-world CMOPs. The experimental results suggest that BiCo can obtain quite competitive performance in comparison to eight state-of-the-art-constrained multiobjective evolutionary optimizers.

Objectives The aim of this study was to explore the difference in target vessel failure (TVF) 3 years after intravascular ultrasound (IVUS) guidance versus angiographic guidance among all comers undergoing second-generation drug-eluting stent (DES) implantation. Background The multicenter randomized ULTIMATE (Intravascular Ultrasound Guided Drug Eluting Stents Implantation in “All-Comers” Coronary Lesions) trial showed a lower incidence of 1-year TVF after IVUS-guided DES implantation among all comers compared with angiographic guidance. However, the 3-year clinical outcomes of the ULTIMATE trial remain unknown. Methods A total of 1,448 all comers undergoing DES implantation who were randomly assigned to either IVUS guidance or angiographic guidance in the ULTIMATE trial were followed for 3 years. The primary endpoint was the risk for TVF at 3 years. The safety endpoint was definite or probable stent thrombosis (ST). Results At 3 years, TVF occurred in 47 patients (6.6%) in the IVUS-guided group and in 76 patients (10.7%) in the angiography-guided group (p = 0.01), driven mainly by the decrease in clinically driven target vessel revascularization (4.5% vs. 6.9%; p = 0.05). The rate of definite or probable ST was 0.1% in the IVUS-guided group and 1.1% in the angiography-guided group (p = 0.02). Notably, the IVUS-defined optimal procedure was associated with a significant reduction in 3-year TVF relative to that with the suboptimal procedure. Conclusions IVUS-guided DES implantation was associated with significantly lower rates of TVF and ST during 3-year follow-up among all comers, particularly those who underwent the IVUS-defined optimal procedure compared with those with angiographic guidance. (Intravascular Ultrasound Guided Drug Eluting Stents Implantation in “All-Comers” Coronary Lesions; NCT02215915 )

This paper studies a multiuser cooperative mobile edge computing offloading (called CoMECO) system in a multiuser interference environment, in which delay-sensitive tasks may be executed on local devices, cooperative devices, or the primary MEC server. In this system, we jointly optimize the offloading decision and computation resource allocation for minimizing the total energy consumption of all mobile users under the delay constraint. If this problem is solved directly, the offloading decision and computation resource allocation are generally generated separately at the same time. Note, however, that they are closely coupled. Therefore, under this condition, their dependency is not well considered, thus leading to poor performance. We transform this problem into a bilevel optimization problem, in which the offloading decision is generated in the upper level, and then the optimal allocation of computation resources is obtained in the lower level based on the given offloading decision. In this way, the dependency between the offloading decision and computation resource allocation can be fully taken into account. Subsequently, a bilevel optimization approach, called BiJOR, is proposed. In BiJOR, candidate modes are first pruned to reduce the number of infeasible offloading decisions. Afterward, the upper-level optimization problem is solved by ant colony system (ACS). Furthermore, a sorting strategy is incorporated into ACS to construct feasible offloading decisions with a higher probability and a local search operator is designed in ACS to accelerate the convergence. For the lower-level optimization problem, it is solved by the monotonic optimization method. In addition, BiJOR is extended to deal with a complex scenario with the channel selection. Extensive experiments are carried out to investigate the performance of BiJOR on two sets of instances with up to 400 mobile users. The experimental results demonstrate the effectiveness of BiJOR and the superiority of the CoMECO system.

Evolutionary many-objective optimization has been gaining increasing attention from the evolutionary computation research community. Much effort has been devoted to addressing this issue by improving the scalability of multiobjective evolutionary algorithms , such as Pareto-based, decomposition-based, and indicator-based approaches. Different from current work, we propose an alternative algorithm in this paper called AnD, which consists of an angle-based selection strategy and a shift-based density estimation strategy. These two strategies are employed in the environmental selection to delete poor individuals one by one. Specifically, the former is devised to find a pair of individuals with the minimum vector angle, which means that these two individuals have the most similar search directions. The latter, which takes both diversity and convergence into account, is adopted to compare these two individuals and to delete the worse one. AnD has a simple structure, few parameters, and no complicated operators. The performance of AnD is compared with that of seven state-of-the-art many-objective evolutionary algorithms on a variety of benchmark test problems with up to 15 objectives. The results suggest that AnD can achieve highly competitive performance. In addition, we also verify that AnD can be readily extended to solve constrained many-objective optimization problems.

A major performance index for analyzing a stochastic transport network is the two-terminal capacity reliability Rd, defined as the probability that d units of goods can be successfully transported via stochastic arc capacities from the source to the destination. This paper presents an efficient d-minimal path method to calculate Rd based on some newly obtained results. The proposed method uses a simple method to check d-minimal path candidates and a more efficient approach to remove duplicate d-minimal paths that are the biggest obstacle in solving d-minimal paths, along with an indication of the advantage over the existing methods. Besides, sensitivity analysis is adopted to explore the most important arc whose reliability change affects the network reliability most significantly, which helps supervisor identify and enhance the critical arcs for improving the network reliability more effectively. Computational and application examples demonstrate the efficiency and utility of the method, respectively.

Constrained multiobjective optimization problems (CMOPs) are frequently encountered in real-world applications, which usually involve constraints in both the decision and objective spaces. However, current artificial CMOPs never consider constraints in the decision space (i.e., decision constraints) and constraints in the objective space (i.e., objective constraints) at the same time. As a result, they have a limited capability to simulate practical scenes. To remedy this issue, a set of CMOPs, named DOC, is constructed in this paper. It is the first attempt to consider both the decision and objective constraints simultaneously in the design of artificial CMOPs. Specifically, in DOC, various decision constraints (e.g., inequality constraints, equality constraints, linear constraints, and nonlinear constraints) are collected from real-world applications, thus making the feasible region in the decision space have different properties (e.g., nonlinear, extremely small, and multimodal). On the other hand, some simple and controllable objective constraints are devised to reduce the feasible region in the objective space and to make the Pareto front have diverse characteristics (e.g., continuous, discrete, mixed, and degenerate). As a whole, DOC poses a great challenge for a constrained multiobjective evolutionary algorithm (CMOEA) to obtain a set of well-distributed and well-converged feasible solutions. In order to enhance current CMOEAs’ performance on DOC, a simple and efficient two-phase framework, named ToP, is proposed in this paper. In ToP, the first phase is implemented to find the promising feasible area by transforming a CMOP into a constrained single-objective optimization problem. Then in the second phase, a specific CMOEA is executed to obtain the final solutions. ToP is applied to four state-of-the-art CMOEAs, and the experimental results suggest that it is quite effective.

Background Double kissing (DK) crush approach for patients with coronary bifurcation lesions, particularly localized at distal left main or lesions with increased complexity, is associated with significant reduction in clinical events when compared with provisional stenting. Recently, randomized clinical trial has demonstrated the net clinical benefits by intravascular ultrasound (IVUS)-guided implantation of drug-eluting stent in all-comers. However, the improvement in clinical outcome after DK crush treatment guided by IVUS over angiography guidance for patients with complex bifurcation lesions have never been studied in a randomized fashion. Trial design DKCRUSH VIII study is a prospective, multicenter, randomized controlled trial designed to assess superiority of IVUS-guided vs angiography-guided DK crush stenting in patients with complex bifurcation lesions according to DEFINITION criteria. A total of 556 patients with complex bifurcation lesions will be randomly (1:1 of ratio) assigned to IVUS-guided or angiography-guided DK crush stenting group. The primary end point is the rate of 12-month target vessel failure, including cardiac death, target vessel myocardial infarction, or clinically driven target vessel revascularization. The secondary end points consist of the individual component of primary end point, all-cause death, myocardial infarction, and in-stent restenosis. The safety end point is the incidence of definite or probable stent thrombosis. An angiographic follow-up will be performed for all patients at 13 months and clinical follow-up will be continued annually until 3 years after the index procedure. Conclusions DKCRUSH VIII trial is the first study designed to evaluate the differences in efficacy and safety between IVUS-guided and angiography-guided DK crush stenting in patients with complex true bifurcation lesions. This study will also provide IVUS-derived criteria to define optimal DK crush stenting for bifurcation lesions at higher complexity.

Aim The present study aimed to assess the benefits of two-stent techniques for patients with DEFINITION criteria-defined complex coronary bifurcation lesions. Methods and results In total, 653 patients with complex bifurcation lesions at 49 international centres were randomly assigned to undergo the systematic two-stent technique (two-stent group) or provisional stenting (provisional group). The primary endpoint was the composite of target lesion failure (TLF) at the 1-year follow-up, including cardiac death, target vessel myocardial infarction (TVMI), and clinically driven target lesion revascularization (TLR). The safety endpoint was definite or probable stent thrombosis. At the 1-year follow-up, TLF occurred in 37 (11.4%) and 20 (6.1%) patients in the provisional and two-stent groups, respectively [77.8%: double-kissing crush; hazard ratio (HR) 0.52, 95% confidence interval (CI) 0.30–0.90; P = 0.019], largely driven by increased TVMI (7.1%, HR 0.43, 95% CI 0.20–0.90; P = 0.025) and clinically driven TLR (5.5%, HR 0.43, 95% CI 0.19–1.00; P = 0.049) in the provisional group. At the 1 year after indexed procedures, the incidence of cardiac death was 2.5% in the provisional group, non-significant to 2.1% in the two-stent group (HR 0.86, 95% CI 0.31–2.37; P = 0.772). Conclusion For DEFINITION criteria-defined complex coronary bifurcation lesions, the systematic two-stent approach was associated with a significant improvement in clinical outcomes compared with the provisional stenting approach. Further study is urgently warranted to identify the mechanisms contributing to the increased rate of TVMI after provisional stenting. Study registration http://www.clinicaltrials.com; Identifier: NCT02284750.

Background Intravascular ultrasound (IVUS)-guided drug-eluting stent (DES) implantation is associated with fewer major adverse cardiovascular events compared with angiography guidance for patients with individual lesion subset. However, the beneficial effect on major adverse cardiovascular event outcome of IVUS guidance over angiography guidance in all-comers who undergo DES implantation still remains understudied. Objectives This study aimed to determine the benefits of IVUS guidance over angiography guidance during DES implantation in all-comer patients. Methods A total of 1,448 all-comer patients who required DES implantation were randomly assigned (1:1 ratio) to either an IVUS guidance or angiography guidance group. The primary endpoint was target-vessel failure (TVF) at 12 months, including cardiac death, target-vessel myocardial infarction, and clinically driven target-vessel revascularization (TVR). The procedure was defined as a success if all IVUS-defined optimal criteria were met. Results At 12 months follow-up, 60 TVFs (4.2%) occurred, with 21 (2.9%) in the IVUS group and 39 (5.4%) in the angiography group (hazard ratio [HR]: 0.530; 95% confidence interval [CI]: 0.312 to 0.901; p = 0.019). In the IVUS group, TVF was recorded in 1.6% of patients with successful procedures, compared with 4.4% in patients who failed to achieve all optimal criteria (HR: 0.349; 95% CI: 0.135 to 0.898; p = 0.029). The significant reduction of clinically driven target-lesion revascularization or definite stent thrombosis (HR: 0.407; 95% CI: 0.188 to 0.880; p = 0.018) based on lesion-level analysis by IVUS guidance was not achieved when the patient-level analysis was performed. Conclusions The present study demonstrates that IVUS-guided DES implantation significantly improved clinical outcome in all-comers, particularly for patients who had an IVUS-defined optimal procedure, compared with angiography guidance. (Intravascular Ultrasound Guided Drug Eluting Stents Implantation in “All-Comers” Coronary Lesions [ULTIMATE]; NCT02215915)

Differential evolution (DE) is a kind of evolutionary algorithms, which is suitable for solving complex optimization problems. Mutation is a crucial step in DE that generates new solutions from old ones. It was argued and has been commonly adopted in DE that the solutions selected for mutation should have mutually different indices. This restrained condition, however, has not been verified either theoretically or empirically yet. In this paper, we empirically investigate the selection of solutions for mutation in DE. From the observation of the extensive experiments, we suggest that the restrained condition could be relaxed for some classical DE versions as well as some advanced DE variants. Moreover, relaxing the restrained condition may also be useful in designing better future DE algorithms.