Author: Community

Cooperative co-evolution (CC) is an efficient and practical evolutionary framework for solving large-scale optimization problems. The performance of CC is affected by the variable decomposition. An accurate variable decomposition can help to improve the performance of CC on solving an optimization problem. The variable grouping methods usually spend many computational resources obtaining an accurate variable decomposition. To reduce the computational cost on the decomposition, we propose an efficient recursive differential grouping (ERDG) method in this article. By exploiting the historical information on examining the interrelationship between the variables of an optimization problem, ERDG is able to avoid examining some interrelationship and spend much less computation than other recursive differential grouping methods. Our experimental results and analysis suggest that ERDG is a competitive method for decomposing large-scale continuous problems and improves the performance of CC for solving the large-scale optimization problems.

Population-based memetic algorithms have been successfully applied to solve many difficult combinatorial problems. Often, a population of fixed size is used in such algorithms to record some best solutions sampled during the search. However, given the particular features of the problem instance under consideration, a population of variable size would be more suitable to ensure the best search performance possible. In this work, we propose a variable population memetic search (VPMS), where a strategic population sizing mechanism is used to dynamically adjust the population size during the search process. Our VPMS approach starts its search from a small population of only two solutions to focus on exploitation and then adapts the population size according to the search status to continuously influence the balancing between exploitation and exploration. We illustrate an application of the VPMS approach to solve the challenging critical node problem (CNP). We show that the VPMS algorithm integrating a variable population, an effective local optimization procedure, and a backbone-based crossover operator performs very well compared to state-of-the-art CNP algorithms. The algorithm is able to discover new upper bounds for 12 instances out of the 42 popular benchmark instances while matching 23 previous best-known upper bounds.

Evolutionary Computation, Volume 28, Issue 4, Page 709-710, Winter 2020.

Many real-world problems contain a large number of decision variables which can be modeled as large scale global optimization (LSGO) problems. One effective way to solve an LSGO problem is to decompose it into smaller subproblems to solve. The existing works mainly focused on designing methods to decompose separable problems, while seldom focused on the decomposition of nonseparable large scale problems. Also, the existing decomposition methods only learn the interaction (correlation or interdependence) among variables to make the decomposition. In this article, we make the decomposition deeper: we not only consider the variable interaction but also take the essentialness of the variable into account to form a deep grouping method. To do this, we first design an essential/trivial variable detection scheme to support the deep decomposition for both separable problems and nonseparable problems. Based on it, we propose a new decomposition method called deep grouping method. Then, we design a new differential evolution (DE) algorithm with a new mutation strategy. By integrating all these, we propose a hybrid deep grouping (HDG) algorithm. Finally, the experiments are conducted on the widely used and most challenging LSGO benchmark suites, and the comparison results of the proposed algorithm with the state-of-the-art algorithms indicate the proposed algorithm is more effective.

Linkage learning techniques are a crucial part of many modern evolutionary methods dedicated to solving problems in discrete domains. Linkage information quality is decisive for the effectiveness of these methods. In this article, we point on two possible linkage inaccuracy types. The missing linkage that occurs when some gene dependencies remain undiscovered, and the false linkage that takes place when linkage identifies gene dependencies that do not exist. To the best of our knowledge, all linkage learning techniques proposed so far are based on predictions, which can commit both of the mistake types. We propose a different approach. Instead of using statistical measures, or evolving the linkage, we check which genes are dependent on one another employing disturbances and the local search. We prove that the proposed technique will never report any false linkage. Thus, the proposed linkage learning based on local optimization (3LO) may miss some linkage but will never report a false one. The main objective of this article is to show the potential brought by 3LO that is fundamentally different from other linkage learning techniques. Since the main disadvantage of the proposed technique is its computational cost, it does not seem suitable for some of the already known, effective evolutionary methods. To overcome this issue, we propose an evolutionary method that employs 3LO. The extensive experimental analysis performed on a large set of hard computational problems shows that the method using 3LO is found to be competitive with other state-of-the-art methods.

The ultimate goal of multiobjective optimization is to help a decision maker (DM) identify solution(s) of interest (SOI) achieving satisfactory tradeoffs among multiple conflicting criteria. This can be realized by leveraging DM’s preference information in evolutionary multiobjective optimization (EMO). No consensus has been reached on the effectiveness brought by incorporating preference in EMO (either a priori or interactively) versus a posteriori decision making after a complete run of an EMO algorithm. Bearing this consideration in mind, this article: 1) provides a pragmatic overview of the existing developments of preference-based EMO (PBEMO) and 2) conducts a series of experiments to investigate the effectiveness brought by preference incorporation in EMO for approximating various SOI. In particular, the DM’s preference information is elicited as a reference point, which represents her/his aspirations for different objectives. The experimental results demonstrate that preference incorporation in EMO does not always lead to a desirable approximation of SOI if the DM’s preference information is not well utilized, nor does the DM elicit invalid preference information, which is not uncommon when encountering a black-box system. To a certain extent, this issue can be remedied through an interactive preference elicitation. Last but not the least, we find that a PBEMO algorithm is able to be generalized to approximate the whole PF given an appropriate setup of preference information.