Author: Community

As one of the classical problems in the economic market, the newsvendor problem aims to make maximal profit by determining the optimal order quantity of products. However, the previous newsvendor models assume that the selling price of a product is a predefined constant and only regard the order quantity as a decision variable, which may result in an unreasonable investment decision. In this article, a new newsvendor model is first proposed, which involves of both order quantity and selling price as decision variables. In this way, the newsvendor problem is reformulated as a mixed-variable nonlinear programming problem, rather than an integer linear programming problem as in previous investigations. In order to solve the mixed-variable newsvendor problem, a histogram model-based estimation of distribution algorithm (EDA) called ${mathrm{ EDA}}_{mvn}$ is developed, in which an adaptive-width histogram model is used to deal with the continuous variables and a learning-based histogram model is applied to deal with the discrete variables. The performance of ${mathrm{ EDA}}_{mvn}$ was assessed on a test suite with eight representative instances generated by the orthogonal experiment design method and a real-world instance generated from real market data of Alibaba. The experimental results show that, ${mathrm{ EDA}}_{mvn}$ outperforms not only the state-of-the-art mixed-variable evolutionary algorithms, but also a commercial software, i.e., Lingo.

There may exist more than one Pareto optimal solution with the same objective vector to a multimodal multiobjective optimization problem (MMOP). The difficulties in finding such solutions can be different. Although a number of evolutionary multimodal multiobjective algorithms (EMMAs) have been proposed, they are unable to solve such an MMOP due to their convergence-first selection criteria. They quickly converge to the Pareto optimal solutions which are easy to find and therefore lose diversity in the decision space. That is, such an MMOP features an imbalance between achieving convergence and preserving diversity in the decision space. In this article, we first present a set of imbalanced distance minimization benchmark problems. Then we propose an evolutionary algorithm using a convergence-penalized density method (CPDEA). In CPDEA, the distances among solutions in the decision space are transformed based on their local convergence quality. Their density values are estimated based on the transformed distances and used as the selection criterion. We compare CPDEA with five state-of-the-art EMMAs on the proposed benchmarks. Our experimental results show that CPDEA is clearly superior in solving these problems.

Optimization methods for spanning tree problems may require efficient data structures. The node-depth-degree representation (NDDR) has achieved relevant results for direct spanning tree representation together with evolutionary algorithms (EAs). Its two mutation operators have average time $O(sqrt {n})$ , where $n$ is the number of vertices of the graph, while similar operators implemented by predecessor arrays, a typical tree data structure, have time $O(n)$ . Dynamic trees are also relevant when investigating tree representations since they have low time complexity, but there is no proper extension of them for EAs. Using aspects of both a dynamic tree and NDDR, namely, Euler tours and structural sharing, we propose a data structure called 2LETT, whose mutation operators have time $O(sqrt {n})$ in the worst case. Experiments with the mutation operators using 2LETT, predecessor arrays, and NDDR are carried out for graphs with up to 300 000 vertices. For a mutation operator that exchanges any two valid edges, predecessor arrays present the best performance for random trees with fewer than 10 000 vertices; while 2LETT has the best performance for trees with more than 10 000 vertices. Especially, noteworthy is the fact that 2LETT is the only structure whose running time is independent of tree diameter.

Presents the table of contents for this issue of the publication.

The objectives of this paper are to investigate the capability of genetic programming to select and extract linearly separable features when the evolutionary process is guided to achieve the same and to propose an integrated system for that. We decompose a $c$ -class problem into $c$ binary classification problems and evolve $c$ sets of binary classifiers employing a steady-state multiobjective genetic programming with three minimizing objectives. Each binary classifier is composed of a binary tree and a linear support vector machine (SVM). The features extracted by the feature nodes and some of the function nodes of the tree are used to train the SVM. The decision made by the SVM is considered the decision of the corresponding classifier. During crossover and mutation, the SVM-weights are used to determine the usefulness of the corresponding nodes. We also use a fitness function based on Golub’s index to select useful features. To discard less frequently used features, we employ unfitness functions for the feature nodes. We compare our method with 34 classification systems using 18 datasets. The performance of the proposed method is found to be better than 432 out of 570, i.e., 75.79% of comparing cases. Our results confirm that the proposed method is capable of achieving our objectives.

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

The performance of decomposition-based multiobjective evolutionary algorithms (MOEAs) often deteriorates clearly when solving multiobjective optimization problems with irregular Pareto fronts (PFs). The main reason is the improper settings of reference vectors and scalarizing functions. In this paper, we propose a decomposition-based MOEA guided by a growing neural gas network, which learns the topological structure of the PF. Both reference vectors and scalarizing functions are adapted based on the topological structure to enhance the evolutionary algorithm’s search ability. The proposed algorithm is compared with eight state-of-the-art optimizers on 34 test problems. The experimental results demonstrate that the proposed method is competitive in handling irregular PFs.

The subgraph isomorphism problems, which aim to map subgraphs to a given graph, are widely seen in many applications and are usually nondeterministic polynomial-time complete (NP-complete). As a representative extension of the subgraph isomorphism problem, virtual network embedding (VNE) is a key problem in datacenter scheduling and network virtualization. Existing metaheuristic approaches to VNE problems tend to schedule networks as a whole. But when the problem scale grows, the performance of these approaches may degenerate due to the curse of dimensionality. In this article, we intend to propose a divide-and-conquer evolutionary algorithm with overlapping decomposition (ODEA) to solve large-scale VNE problems. First, realizing the fact that the decision variables in graph-based optimization problems like VNE are usually nonseparable, an overlapping decomposition method is introduced by investigating the characteristic of the network structure. In this method, the critical elements which have tight connections to many other nodes can belong to multiple subcomponents. As a result, the decision variables with tight connections can always be evolved together in multiple subcomponents. Second, to combine the subsolutions into a complete feasible solution, a competitive strategy is devised. Through the competition among critical elements, the optimizing information is shared among subcomponents, which can further improve the effectiveness of ODEA. The proposed ODEA can adopt different metaheuristics as the optimizer, and we conduct experiments on both the scenarios with a single virtual network and with a series of online networks. The experimental results verify that ODEA can significantly improve the performance of different metaheuristics in large-scale VNE problems.

For complex system design [e.g., satellite layout optimization design (SLOD)] in practical engineering, when launching a new optimization instance with another parameter configuration from the intuition of designers, it is always executed from scratch which wastes much time to repeat the similar search process. Inspired by transfer learning which can reuse past experiences to solve relevant tasks, many researchers pay more attention to explore how to learn from past optimization instances to accelerate the target one. In real-world applications, there have been numerous similar source instances stored in the database. The primary question is how to measure the transferability from numerous sources to avoid the notorious negative transferring. To obtain the relatedness between source and target instance, we develop an optimization instance representation method named centroid distribution, which is by the aid of the probabilistic model learned by elite candidate solutions in estimation of distribution algorithm (EDA) during the evolutionary process. Wasserstein distance is employed to evaluate the similarity between the centroid distributions of different optimization instances, based on which, we present a novel framework called multisource selective transfer optimization with three strategies to select sources reasonably. To choose the suitable strategy, four selection suggestions are summarized according to the similarity between the source and target centroid distribution. The framework is beneficial to choose the most suitable sources, which could improve the search efficiency in solving multiobjective optimization problems. To evaluate the effectiveness of the proposed framework and selection suggestions, we conduct two experiments: 1) comprehensive empirical studies on complex multiobjective optimization problem benchmarks and 2) a real-world SLOD problem. Suggestions for strategy selection coincide with the experiment results, based on which, we propose a mi-
ed strategy to deal with the negative transfer in the experiments successfully. The results demonstrate that our proposed framework achieves competitive performance on most of the benchmark problems in convergence speed and hypervolume values and performs best on the real-world applications among all the comparison algorithms.

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.