Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Palace: A Parallel Adaptive Local-Search Algorithm Based on Competition and Evolution for Orienteering Problems and Variants

Version 1 : Received: 20 April 2023 / Approved: 21 April 2023 / Online: 21 April 2023 (03:17:49 CEST)

How to cite: Lei, H.; Du, Y.; Xing, L.; Chen, Y. Palace: A Parallel Adaptive Local-Search Algorithm Based on Competition and Evolution for Orienteering Problems and Variants. Preprints 2023, 2023040663. https://doi.org/10.20944/preprints202304.0663.v1 Lei, H.; Du, Y.; Xing, L.; Chen, Y. Palace: A Parallel Adaptive Local-Search Algorithm Based on Competition and Evolution for Orienteering Problems and Variants. Preprints 2023, 2023040663. https://doi.org/10.20944/preprints202304.0663.v1

Abstract

A number of challenging combinatorial optimization problems in logistics, transportations, aeronautics, and astronautics can be modeled as orienteering problems (OPs). To address the classic OP and its real-world variants, a parallel adaptive local-search algorithm based on competition and evolution (Palace) is proposed in this paper. In this algorithm, the parallelism runs proper local-search metaheuristics and operators to obtain the population per generation; then the competition grades those metaheuristics and operators to highlight the outperforming and eliminate the underperforming; also, the evolution explores large solution space and reproduces the best solutions for next generation. In this manner, the parallelism, competition, and evolution are organized in an easy-to-use algorithm and enable the expansibility, adaptivity, and exploration abilities, respectively. The Palace is examined on the classic and real-world Benchmarks about the OP, the time-dependent/independent OP with time windows, and the unmanned aerial vehicle and agile earth observation satellite planning. As a result, the Palace shows good performance in applicability and effectiveness in comparison with the state-of-the-art algorithms.

Keywords

Orienteering problems; local-search metaheuristics; parallelism; competition; evolution; Benchmark

Subject

Computer Science and Mathematics, Discrete Mathematics and Combinatorics

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