Siwei Jiang

Consistencies and contradictions of performance metrics in multiobjective optimization.

An important consideration of multiobjective optimization (MOO) is the quantitative metrics used for defining the optimality of different solution sets, which is also the basic principle for the design and evaluation of MOO algorithms. Although a plethora of performance metrics have been proposed in the MOO context, there has been a lack of insights on the relationships between metrics. In this paper, we first group the major MOO metrics proposed to date according to four core performance criteria considered in the literature, namely, capacity, convergence, diversity, and convergence-diversity. Then, a comprehensive study is conducted to investigate the relationships among representative group metrics, including generational distance, E-indicator (I1∈+), spread (Δ), generalized spread (Δ*), inverted generational distance, and hypervolume. Experimental results indicated that these six metrics show high consistencies when Pareto fronts (PFs) are convex, whereas they show certain contradictions on concave PFs.

A Simple and Fast Hypervolume Indicator-Based Multiobjective Evolutionary Algorithm

To find diversified solutions converging to true Pareto fronts (PFs), hypervolume (HV) indicator-based algorithms have been established as effective approaches in multiobjective evolutionary algorithms (MOEAs). However, the bottleneck of HV indicator-based MOEAs is the high time complexity for measuring the exact HV contributions of different solutions. To cope with this problem, in this paper, a simple and fast hypervolume indicator-based MOEA (FV-MOEA) is proposed to quickly update the exact HV contributions of different solutions. The core idea of FV-MOEA is that the HV contribution of a solution is only associated with partial solutions rather than the whole solution set. Thus, the time cost of FV-MOEA can be greatly reduced by deleting irrelevant solutions. Experimental studies on 44 benchmark multiobjective optimization problems with 2-5 objectives in platform jMetal demonstrate that FV-MOEA not only reports higher hypervolumes than the five classical MOEAs (nondominated sorting genetic algorithm II (NSGAII), strength Pareto evolutionary algorithm 2 (SPEA2), multiobjective evolutionary algorithm based on decomposition (MOEA/D), indicator-based evolutionary algorithm, and S-metric selection based evolutionary multiobjective optimization algorithm (SMS-EMOA)), but also obtains significant speedup compared to other HV indicator-based MOEAs.

Robustness of Trust Models and Combinations for Handling Unfair Ratings

In electronic marketplaces, after each transaction buyers will rate the products provided by the sellers. To decide the most trustworthy sellers to transact with, buyers rely on trust models to leverage these ratings to evaluate the reputation of sellers. Although the high effectiveness of different trust models for handling unfair ratings have been claimed by their designers, recently it is argued that these models are vulnerable to more intelligent attacks, and there is an urgent demand that the robustness of the existing trust models has to be evaluated in a more comprehensive way. In this work, we classify the existing trust models into two broad categories and propose an extendable e-marketplace testbed to evaluate their robustness against different unfair rating attacks comprehensively. On top of highlighting the robustness of the existing trust models for handling unfair ratings is far from what they were claimed to be, we further propose and validate a novel combination mechanism for the existing trust models, Discount-then-Filter, to notably enhance their robustness against the investigated attacks.

Pareto Rank Learning in Multi-objective Evolutionary Algorithms

In this paper, the interest is on cases where assessing the goodness of a solution for the problem is costly or hazardous to construct or extremely computationally intensive to compute. We label such category of problems as “expensive” in the present study. In the context of multi-objective evolutionary optimizations, the challenge amplifies, since multiple criteria assessments, each defined by an “expensive” objective is necessary and it is desirable to obtain the Pareto-optimal solution set under a limited resource budget. To address this issue, we propose a Pareto Rank Learning scheme that predicts the Pareto front rank of the offspring in MOEAs, in place of the “expensive” objectives when assessing the population of solutions. Experimental study on 19 standard multi-objective benchmark test problems concludes that Pareto rank learning enhanced MOEA led to significant speedup over the state-of-the-art NSGA-II, MOEA/D and SPEA2.

Asymmetric pareto-adaptive scheme for multiobjective optimization

A core challenge of Multiobjective Evolutionary Algorithms (MOEAs) is to attain evenly distributed Pareto optimal solutions along the Pareto front. In this paper, we propose a novel asymmetric Pareto-adaptive (apa ) scheme for the identification of well distributed Pareto optimal solutions based on the geometrical characteristics of the Pareto front. The apa scheme applies to problem with symmetric and asymmetric Pareto fronts. Evaluation on multiobjective problems with Pareto fronts of different forms confirms that apa improves both convergence and diversity of the classical decomposition-based (MOEA/D) and Pareto dominance-based MOEAs (pae -MyDE).

Multiobjective optimization based on reputation

To improve the robustness and ease-of-use of Evolutionary Algorithms (EAs), adaptation on evolutionary operators and control parameters shows significant advantages over fixed operators with default parameter settings. To date, many successful research efforts to adaptive EAs have been devoted to Single-objective Optimization Problems (SOPs), whereas, few studies have been conducted on Multiobjective Optimization Problems (MOPs). Directly inheriting the adaptation mechanisms of SOPs in the MOPs context faces challenges due to the intrinsic differences between these two kinds of problems. To fill in this gap, in this paper, a novel Multiobjective Evolutionary Algorithm (MOEA) based on reputation is proposed as a unified framework for general MOEAs. The reputation concept is introduced for the first time to measure the dynamic competency of evolutionary operators and control parameters across problems and stages of the search in MOEAs. Based on the notion of reputation, individual solutions then select highly reputable evolutionary operators and control parameters. Experimental studies on 58 benchmark MOPs in jMetal confirm its superior performance over the classical MOEAs and other adaptive MOEAs.

A Unified Framework for Vehicle Rerouting and Traffic Light Control to Reduce Traffic Congestion

As the number of vehicles grows rapidly each year, more and more traffic congestion occurs, becoming a big issue for civil engineers in almost all metropolitan cities. In this paper, we propose a novel pheromone-based traffic management framework for reducing traffic congestion, which unifies the strategies of both dynamic vehicle rerouting and traffic light control. Specifically, each vehicle, represented as an agent, deposits digital pheromones over its route, while roadside infrastructure agents collect the pheromones and fuse them to evaluate real-time traffic conditions as well as to predict expected road congestion levels in near future. Once road congestion is predicted, a proactive vehicle rerouting strategy based on global distance and local pheromone is employed to assign alternative routes to selected vehicles before they enter congested roads. In the meanwhile, traffic light control agents take online strategies to further alleviate traffic congestion levels. We propose and evaluate two traffic light control strategies, depending on whether or not to consider downstream traffic conditions. The unified pheromone-based traffic management framework is compared with seven other approaches in simulation environments. Experimental results show that the proposed framework outperforms other approaches in terms of traffic congestion levels and several other transportation metrics, such as air pollution and fuel consumption. Moreover, experiments over various compliance and penetration rates show the robustness of the proposed framework.

Autoencoding Evolutionary Search With Learning Across Heterogeneous Problems

To enhance the search performance of evolutionary algorithms, reusing knowledge captured from past optimization experiences along the search process has been proposed in the literature, and demonstrated much promise. In the literature, there are generally three types of approaches for reusing knowledge from past search experiences, namely exact storage and reuse of past solutions, the reuse of model-based information, and the reuse of structured knowledge captured from past optimized solutions. In this paper, we focus on the third type of knowledge reuse for enhancing evolutionary search. In contrast to existing works, here we focus on knowledge transfer across heterogeneous continuous optimization problems with diverse properties, such as problem dimension, number of objectives, etc., that cannot be handled by existing approaches. In particular, we propose a novel autoencoding evolutionary search paradigm with learning capability across heterogeneous problems. The essential ingredient for learning structured knowledge from search experience in our proposed paradigm is a single layer denoising autoencoder (DA), which is able to build the connections between problem domains by treating past optimized solutions as the corrupted version of the solutions for the newly encountered problem. Further, as the derived DA holds a closed-form solution, the corresponding reusing of knowledge from past search experiences will not bring much additional computational burden on the evolutionary search. To evaluate the proposed search paradigm, comprehensive empirical studies on the complex multiobjective optimization problems are presented, along with a real-world case study from the fiber-reinforced polymer composites manufacturing industry.

An empirical study of multifactorial PSO and multifactorial DE

Recently, the notion of Multifactorial Optimization (MFO) has emerged as a promising approach for evolutionary multi-tasking by automatically exploiting the latent synergies between optimization problems, simply through solving them together in an unified representation space [1]. It aims to improve convergence characteristics across multiple optimization problems at once by seamlessly transferring knowledge between them. In [1], the efficacy of MFO has been studied by a specific mode of knowledge transfer in the form of implicit genetic transfer through chromosomal crossover. Here we further explore the generality of MFO when diverse population based search mechanisms are employed. In particular, in this paper, we present the first attempt to conduct MFO with the popular particle swarm optimization and differential evolution search. Two specific multi-tasking paradigms, namely multifactorial particle swarm optimization (MFPSO) and multifactorial differential evolution (MFDE) are proposed. To evaluate the performance of MFPSO and MFDE, comprehensive empirical studies on 9 single objective MFO benchmark problems are provided.

Adaptive indicator-based evolutionary algorithm for multiobjective optimization problems

Indicator-based evolutionary algorithm (IBEA1) is a fast and effective approach for solving multiobjective optimization problems (MOPs). In the classical IBEA1, the parameter κ is predefined to amplify or shrink the indicator differences on pairwise solutions. However, the value of κ in IBEA1 needs to be carefully calibrated based on the selected indicator (e.g., hypervolume or additive e-indicator) and the encountered MOPs. In this paper, a new version of IBEA1 (labeled as IBEA2 hereafter) is proposed to adaptively adjust parameter κ for solving various MOPs. The core idea of IBEA2 is to adapt parameter κ for the purpose of selecting the subset of offspring solutions with the maximum hypervolume into the next population. Experimental studies on 44 benchmark MOPs with 2-5 objectives in jMetal verified that IBEA2 is able to find higher hypervolumes against the four classical MOEAs, which are NSGAII, SPEA2, MOEA/D and IBEA1, in the literature.