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A novel lossless image compression algorithm using multiset permutation encoding

International Journal of Information Technology (Singapore) 2025年第1期17卷 97-106页

作者： Auwatanamongkol, Surapong School of Applied Statistics National Institute of Development Administration Bangkok Thailand

In this paper, a new lossless image compression algorithm is proposed. The algorithm is based on the fact that the number of possible permutations for a binary multiset with a fixed number of elements gets smaller when the number of ‘1’ bits in the multiset approaches zero. Therefore, a particular permutation of the multiset can be encoded using few bits if the number of ‘1’ bits in the multiset is small. To enhance the compression performance of the encoding, pixel color transformation is performed as a preprocessing step to eliminate inter-pixel redundancies as well as assign each pixel value a new value that has a number of bits of ‘1’ as few as possible (depending on the frequency of the pixel value in the image). Additional compression techniques such as a variable length encoding and a run length encoding are also incorporated to enhance compression performance of the encoding process. Based on experiments, the performance of the proposed algorithm is comparable to many popular lossless image compression algorithms. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.

关键词： Lossless image compression permutation encoding Pixel color transformation

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Artificial bee colony algorithm using permutation encoding for the bounded diameter minimum spanning tree problem

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SOFT COMPUTING 2021年第16期25卷 11289-11305页

作者： Singh, Kavita Sundar, Shyam Natl Inst Technol Raipur Dept Comp Applicat Raipur 492010 Madhya Pradesh India

The bounded diameter minimum spanning tree (BD-MST) problem seeks a spanning tree (T) of minimum weight on a given connected, undirected and edge-weighted graph subject to the diameter of T does not exceed D >= 2, where D is a given positive integer. The BD-MST problem isNP-hard problem and finds many real-world applications. In this paper, we propose an artificial bee colony (ABC) algorithm for the BD-MST problem. ABC algorithm is a swarm-based metaheuristic technique based on the intelligent foraging behavior of honeybees. The proposed ABC algorithm employs permutation encoding. To exploit this encoding structure, two neighborhood strategies that help ABC algorithm in faster convergence towards finding high quality solutions are applied. On a set of Euclidean and non-Euclidean benchmark instances for various diameter bounds, the proposed approach has been compared with state-of-the-art approaches. Computational results demonstrate the effectiveness of the proposed approach to the other extant approaches in the literature.

关键词： Spanning tree Bounded-diameter Swarm intelligence Artificial bee colony algorithm permutation encoding Neighborhood strategies

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Effective permutation encoding for Evolutionary Optimization of the Electric Vehicle Routing Problem

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ENERGIES 2021年第20期14卷 6651页

作者： Iwankowicz, Remigiusz Maritime Univ Szczecin Fac Econ & Transport Engn PL-70500 Szczecin Poland

This paper addresses the problem of route planning for a fleet of electric vehicles departing from a depot and supplying customers with certain goods. This paper aims to present a permutation-based method of vehicle route coding adapted to the specificity of electric drive. The developed method integrated with an evolutionary algorithm allows for rapid generation of routes for multiple vehicles taking into account the necessity of supplying energy in available charging stations. The minimization of the route distance travelled by all vehicles was taken as a criterion. The performed testing indicated satisfactory computation speed. A real region with four charging stations and 33 customers was analysed. Different scenarios of demand were analysed, and factors affecting the results of the proposed calculation method were indicated. The limitations of the method were pointed out, mainly caused by assumptions that simplify the problem. In the future, it is planned for research and method development to include the lapse of time and for the set of factors influencing energy consumption by a moving vehicle to be extended.

关键词： electric vehicles fleet routing charging stations placement evolutionary optimization permutation encoding

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Particle Swarm Optimization Based on Neighborhood encoding for Traveling Salesman Problem

Particle Swarm Optimization Based on Neighborhood Encoding f...

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IEEE International Conference on System, Man, and Cybernetic

作者： Lin, Dongmei Qiu, Shenshan Wang, Dong Foshan Univ Ctr Informat & Educ Technol Foshan 528000 Guangdong Peoples R China South China Univ Technol Coll Automat Sci & Engn Guangzhou 510641 Guangdong Peoples R China Foshan Univ Dept Comp Sci & Technol Guangzhou 528000 Guangdong Peoples R China

ISBN: (纸本)9781424423835

While applying particle swarm optimization to solving traveling salesman problem, some encoding schemes can't utilize well flying character. Particle swarm optimization based on neighborhood encoding for solving traveling salesman problem is put forward in this paper. Scheme of neighborhood encoding makes particles flying efficaciously to interesting region, and eventually makes particle swarm optimization converging with higher efficiency than those utilizing other encoding schemes. Simulation results indicate that new encoding scheme is efficacious.

关键词： particle swarm optimization traveling salesman problem permutation encoding neighborhood encoding particle flying character

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Discrete cuckoo search algorithms for test case prioritization

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APPLIED SOFT COMPUTING 2021年 110卷 107584-107584页

作者： Bajaj, Anu Sangwan, Om Prakash Machine Intelligence Res Labs MIR Labs Sci Network Res Excellence Auburn WA 98071 USA Guru Jambheshwar Univ Sci & Technol Dept Comp Sci & Engn Hisar 125001 Haryana India

Regression testing is an essential aspect of the software development lifecycle. As the software evolves, the test suite grows, hence the cost and effort to retest the software. Test case prioritization is one of the mitigation techniques for regression testing. It ranks the test cases to maximize the desired properties, e.g., detecting faults early. The efficiency and effectiveness of test case prioritization techniques can be enhanced using optimization algorithms. Nature-inspired algorithms are gaining more attention due to their easy implementation and quality of the solutions. This paper proposes the discrete cuckoo search algorithm for test case prioritization. The prioritization problem deals with ordering the test cases. Therefore, a new adaptation strategy using asexual genetic reproduction is introduced to convert real numbers into permutation sequences. Furthermore, the cuckoo search algorithm's effectiveness is extended with the genetic algorithm's mutation operator to balance the trade-off between exploration and exploitation. An in-depth comparative study on four case studies is conducted between the proposed algorithms, existing state-of-the-art algorithms and baseline approach. Statistical investigation confirms that the proposed hybrid cuckoo search algorithm outperforms the genetic algorithm, particle swarm optimization, ant colony optimization, tree seed algorithm and random search by 4.29%, 5.52%, 8.38%, 2.74% and 10.80%, respectively. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Search based software testing Regression testing Test case prioritization Meta-heuristics Nature-inspired algorithms Cuckoo search algorithm Asexual reproduction algorithm permutation encoding

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Improved genetic algorithm based on rule optimization strategy for fibre allocation

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SYSTEMS SCIENCE & CONTROL ENGINEERING 2024年第1期12卷

作者： Tan, Feng Yuan, Zhipeng Zhang, Yong Tang, Sheng Guo, Feng Zhang, Shuai Jiangsu Zhongtian Technol Co Ltd Nantong Jiangsu Peoples R China Wuhan Univ Sci & Technol Sch Informat Sci & Engn Wuhan Peoples R China Minist Educ Engn Res Ctr Met Automat & Measurement Technol Wuhan Peoples R China Yuanshi Auton Co Ltd Nantong Peoples R China

In modern manufacturing industry, in order to adapt to changes in the general environment, the manufacturing industry must improve production efficiency. To this end, this article introduces an improved genetic algorithm based on rule selection to tackle the nondeterministic polynomial hard problem stemming from inventory fibre resources and fibre selection principles in optical cable production. The algorithm aims to maximize inventory score and minimize fibre segmentation rate. It employs a permutation encoding approach to link the genetic algorithm with fibre allocation solutions and applies a self-attention mechanism to determine subset solution weight within each solution. To boost the recombination of favourable gene segments from different chromosomes, a rule optimization strategy is integrated into the crossover operation based on the weights. This operations enhance the algorithm's global search capability and convergence speed. A feasibility repair strategy is then used to inspect and rectify chromosomes, preventing the generation of illegal solutions. The legitimate mutation operation, founded on weight optimization rules, effectively reduces the algorithm's running time by avoiding illegal solutions. By leveraging actual production data from an optical cable manufacturer for simulation, the experimental results confirm the effectiveness of the improved genetic algorithm in addressing the fibre allocation problem. Comparative simulations with the unimproved genetic algorithm and a stepwise greedy algorithm underscore the superiority of the improved genetic algorithm in resolving the fibre allocation problem.

关键词： Fibre segmentation rate self-attention mechanism permutation encoding feasibility repair genetic algorithm

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Graph Matching with Distance-Preserving Crossover

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COMPLEX SYSTEMS 2022年第2期31卷 219-246页

作者： Barecke, Thomas Detyniecki, Marcin Univ Paris 06 Databases & Machine Learning Dept Lab Informat Paris 6 F-75005 Paris France

Graph models are fundamental to any kind of application on structured real-world problems. Any comparison between graphs by a graph distance measure requires the solution of the inexact graph matching problem, which constitutes a hard combinatorial optimization problem. An inexact matching problem includes in its formulation robustness to any type of perturbation, such as, for instance, noise, inherently present in real-world environments. In this paper, we introduce the concept of distance-preserving crossover operators for genetic algorithms for this task. For large graphs, our algorithm outperforms any state-of-the-art approximate algorithm-in particular, genetic algorithms with alternative crossover operators, which are to the best of our knowledge currently limited to no more than 50 nodes. We use a two-level local search heuristic to further enhance the results, pushing the limits to up to 300 nodes: a first local search step is directly integrated into the crossover operator;another one is applied independently during offspring generation.

关键词： graph distance evolutionary computation genetic algorithm inexact graph isomorphism permutation encoding crossover operator with local search

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