On Improving Adaptive Problem Decomposition Using Differential Evolution for Large-Scale Optimization Problems

Abstract

Modern computational mathematics and informatics for Digital Environments deal with the high dimensionality when designing and optimizing models for various real-world phenomena. Large-scale global black-box optimization (LSGO) is still a hard problem for search metaheuristics, including bio-inspired algorithms. Such optimization problems are usually extremely multi-modal, and require significant computing resources for discovering and converging to the global optimum. The majority of state-of-the-art LSGO algorithms are based on problem decomposition with the cooperative co-evolution (CC) approach, which divides the search space into a set of lower dimensional subspaces (or subcomponents), which are expected to be easier to explore independently by an optimization algorithm. The question of the choice of the decomposition method remains open, and an adaptive decomposition looks more promising. As we can see from the most recent LSGO competitions, winner-approaches are focused on modifying advanced DE algorithms through integrating them with local search techniques. In this study, an approach that combines multiple ideas from state-of-the-art algorithms and implements Coordination of Self-adaptive Cooperative Co-evolution algorithms with Local Search (COSACC-LS1) is proposed. The self-adaptation method tunes both the structure of the complete approach and the parameters of each algorithm in the cooperation. The performance of COSACC-LS1 has been investigated using the CEC LSGO 2013 benchmark and the experimental results has been compared with leading LSGO approaches. The main contribution of the study is a new self-adaptive approach that is preferable for solving hard real-world problems because it is not overfitted with the LSGO benchmark due to self-adaptation during the search process instead of a manual benchmark-specific fine-tuning.

Applied and Computational Mathematics for Digital Environments

“…A total of 12 papers were submitted to this Special Issue, of which 11 were published (91.67%) [11][12][13][14][15][16][17][18][19][20][21] and only 1 was rejected (8.33%), indicating the very high quality of the original submissions.…”

“…Vakhnin et al [20] address large-scale global black-box optimization (LSGO). The authors propose a self-adaptive approach that combines ideas from state-of-the-art algorithms and implements Coordination of Self-adaptive Cooperative Co-evolution algorithms with Local Search (COSACC-LS1).…”

Metaheuristics in Business Model Development for Local Tourism Sustainability Enhancement

“…Consequently, the original structure was required to improve its performance. Various studies have investigated the DE approach, and it has been used extensively in multiple problems, such as a workforce scheduling and routing problem in a sugarcane mill [15], a multi-trip vehicle routing problem with backhauls and a heterogeneous fleet in the beverage logistics industry [16], a large-scale global black-box optimization problem [17], a cyclical multiple parallel machine scheduling problem in sugarcane unloading systems [18], and an employee transportation problem [19,20]. The variable neighborhood search algorithm is a metaheuristic that uses the idea of neighborhood change, which has more than one type of neighborhood structure, to systematically explore the solution space [18,21].…”

Dynamic Population on Bio-Inspired Algorithms Using Machine Learning for Global Optimization

“…We have, therefore, chosen the harder functions of the CEC LSGO suite [ 17 ] as the testing ground for our proposition. These functions, renowned in the optimization community, embody a myriad of challenges, from multi-modality to shifting landscapes, serving as an ideal crucible to truly assess the mettle of our strategy [ 18 , 19 , 20 ]. the CEC LSGO suite, with its diverse and demanding function set, offers a comprehensive canvas, enabling us to probe the strengths and potential limitations of our approach under varied conditions.…”