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membrane algorithm with Brownian subalgorithm and genetic subalgorithm

INTERNATIONAL JOURNAL OF FOUNDATIONS OF COMPUTER SCIENCE 2007年第6期18卷 1353-1360页

作者： Nishida, Taishin Y. Toyama Prefectural Univ Fac Engn Toyama 9390398 Japan

membrane algorithms with subalgorithms inspired by simulated annealing are treated in this paper. Simulated annealing is inherently a kind of local search but it modifies a solution to a worse one with a probability determined by "temperature". The temperature of simulated annealing is changed according to "cooling schedule". On the other hand, the subalgorithm introduced here has constant temperature which is determined by the region where the subalgorithm is. It is called Brownian subalgorithm since the subalgorithm incorporates "thermal movement" of a solution in the search space but does not simulate "annealing". Computer simulations show that a membrane algorithm which has three regions and has a Brownian subalgorithm in each region can obtain very good approximate solutions for several benchmark problems of the traveling salesman problem. However, the algorithm, occasionally, gets quite bad solutions (twice as large as the optimum) for a problem. A membrane algorithm which has both Brownian and genetic subalgorithms never gets such a bad solution (only 8% worse than the optimum) for all problems examined, although, in average, it is not as good as the algorithm with Brownian only. The result indicates that membrane algorithm with subalgorithms under different approximate mechanisms may be robust under a wide range of problems.

关键词： membrane algorithm simulated annealing local search traveling salesman problem

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A multi-objective membrane algorithm guided by the skin membrane

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NATURAL COMPUTING 2016年第4期15卷 597-610页

作者： Zhang, Xingyi Li, Jun Zhang, Lei Anhui Univ Inst Bioinspired Intelligence & Min Knowledge Sch Comp Sci & Technol Hefei 230039 Peoples R China

Multi-objective optimization problems exist widely in the field of engineering and science. Many nature-inspired methods, such as genetic algorithms, particle swarm optimization algorithms and membrane computing model based algorithms, were proposed to solve the problems. Among these methods, membrane computing model based algorithms, also termed membrane algorithms, are becoming a current research hotspot because the successful linkage of membrane computing and evolutionary algorithms. In the past years, a lot of effective multi-objective membrane algorithms have been designed, where the skin membrane was often only used as an archive to store good solutions. In this paper, we propose an effective multi-objective membrane algorithm guided by the skin membrane, named SMG-MOMA, where the information of solutions stored in the skin membrane is used to guide the evolution of internal membranes. A skin membrane guiding strategy is suggested by allocating the solutions in skin membrane to internal membranes. Experimental results on ZDT and DTLZ benchmark multi-objective problems show that the proposed algorithm outperforms the-state-of-the-art multi-objective optimization algorithms.

关键词： membrane computing Multi-objective optimization membrane algorithm Archive

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A novel membrane algorithm for capacitated vehicle routing problem

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SOFT COMPUTING 2015年第2期19卷 471-482页

作者： Niu, Yunyun Wang, Shuo He, Juanjuan Xiao, Jianhua Peking Univ Sch Elect Engn & Comp Sci Beijing 100871 Peoples R China Huazhong Univ Sci & Technol Dept Control Sci & Engn Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Nankai Univ Res Ctr Logist Tianjin 300071 Peoples R China

This study is focused on solving the capacitated vehicle routing problem (CVRP) by applying a novel membrane algorithm based on ant colony optimization (MA_ACO). The effect of non-determinism on the performance of the membrane algorithm is also studied in this work. In this approximate approach model, a membrane system is considered to be a non-deterministic distributed parallel framework, and ant colony optimization (ACO) is used as a sub-algorithm of elementary membranes. With the purpose of maintaining balance between the convergence rate and the population diversity, MA_ACO supports sub-algorithms for elementary membranes based on two types of ACO. The elementary membranes send their best solutions to the skin membrane. In the next step, the best one in the skin membrane is sent back to every inner membrane with a fixed probability. All of the elementary membranes have thus a chance to receive the best result and make changes to the current search direction. Thirty benchmark problems of CVRP are utilized to confirm the effectiveness of the proposed membrane algorithm. Experimental results show that compared with other algorithms proposed in the previous literature, our algorithm is very competitive. The new best solutions for seven instances are also listed.

关键词： membrane algorithm Ant colony optimization Max-min ant system Capacitated vehicle routing problem

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A skin membrane-driven membrane algorithm for many-objective optimization

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NEURAL COMPUTING & APPLICATIONS 2018年第1期30卷 141-152页

作者： Li, Zhangxiao Zhang, Lei Su, Yansen Li, Jun Wang, Xun Huishang Vocat Coll Dept Accounting Hefei 230032 Anhui Peoples R China Anhui Univ Sch Comp Sci & Technol Hefei 230039 Anhui Peoples R China China Univ Petr Coll Comp & Commun Engn Qingdao 266580 Peoples R China

Many-objective optimization problems refer to problems that hold more than three conflicting objectives, which are more challenging than those with two or three objectives. membrane computing models, usually termed P systems, are a class of living cell-inspired computing models, which provide a rich framework for solving a variety of challenging problems. In this paper, a membrane computing model-based algorithm is proposed for many-objective optimization. Specifically, the population in the skin membrane is divided into two subpopulations, one used for guiding the convergence of populations in the internal membrane, while the other taking charge of the diversity of populations. Experimental results on benchmark test problems for many-objective optimization indicate the superiority of the developed algorithm over existing evolutionary many-objective optimization algorithms and P systems based multi-objective optimization algorithms.

关键词： membrane computing Many-objective optimization membrane algorithm Archive

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AN ADAPTIVE membrane algorithm FOR SOLVING COMBINATORIAL OPTIMIZATION PROBLEMS

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Acta Mathematica Scientia 2014年第5期34卷 1377-1394页

作者：贺娟娟肖建华邵泽辉 Key Laboratory of Image Processing and Intelligent Control School of AutomationHuazhong University of Science and Technology The Research Center of Logistics Nankai University School of Information Science and Technology Chengdu University

membrane algorithms （MAs）, which inherit from P systems, constitute a new parallel and distribute framework for approximate computation. In the paper, a membrane algorithm is proposed with the improvement that the involved parameters can be adaptively chosen. In the algorithm, some membranes can evolve dynamically during the computing process to specify the values of the requested parameters. The new algorithm is tested on a well-known combinatorial optimization problem, the travelling salesman problem. The em-pirical evidence suggests that the proposed approach is efficient and reliable when dealing with 11 benchmark instances, particularly obtaining the best of the known solutions in eight instances. Compared with the genetic algorithm, simulated annealing algorithm, neural net-work and a fine-tuned non-adaptive membrane algorithm, our algorithm performs better than them. In practice, to design the airline network that minimize the total routing cost on the CAB data with twenty-five US cities, we can quickly obtain high quality solutions using our algorithm.

关键词： membrane algorithm adaptation travelling salesman problem

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A new membrane algorithm using the rules of Particle Swarm Optimization incorporated within the framework of cell-like P-systems to solve Sudoku

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APPLIED SOFT COMPUTING 2016年 45卷 27-39页

作者： Singh, Garima Deep, Kusum Indian Inst Technol Dept Math Roorkee 247667 Uttar Pradesh India

Sudoku, of order n, is a combinatorial puzzle having partially filled n(2) x n(2) grid consisting of sub-grids of n x n dimension. In this paper, a new membrane algorithm, namely MA_PSO_M, is presented. It uses the modified rules of Particle Swarm Optimization coupled with a carefully designed mutation operator within the framework of cell-like P-systems. Another significant contribution of this paper is the novel way in which the search space for solving the Sudoku problem is defined. Initially, the proposed algorithm is used to solve Sudoku puzzles of order 3 available in literature. On the basis of experiments performed on sample Sudoku puzzles of 'easy' and 'medium' difficulty levels it is concluded that the proposed membrane algorithm, MA_PSO_M, is very efficient and reliable. For the 'hard' and 'evil' difficultly levels, too the algorithm performs very well after incorporating an additional deterministic phase. The performance of the algorithm is further enhanced with an increased population size in a very small computational time. To further demonstrate efficiency of algorithm it is applied to Sudoku puzzles of order 4. The obtained results prove that the proposed membrane algorithm clearly dominates any of the PSO based membrane algorithm existing in the literature. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Sudoku membrane algorithm P-system Particle Swarm Optimization

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A membrane algorithm with quantum-inspired subalgorithms and its application to image processing

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NATURAL COMPUTING 2012年第4期11卷 701-717页

作者： Zhang, Gexiang Gheorghe, Marian Li, Yuquan SW Jiaotong Univ Sch Elect Engn Chengdu 610031 Sichuan Peoples R China Univ Sheffield Dept Comp Sci Sheffield S1 4DP S Yorkshire England Univ Pitesti Dept Comp Sci Pitesti 110040 Romania

This paper presents a membrane algorithm, called MAQIS, by appropriately combining concepts and principles of membrane computing and quantum-inspired evolutionary approach. MAQIS has four distinct features from the membrane algorithms reported in the literature: initial solutions are only inside the skin membrane;different regions separated by membranes have different components of the algorithm;all the components inside membranes cooperate to produce offspring in a single evolutionary generation;communication rules are performed in a single evolutionary step. Extensive experiments conducted on knapsack problems show that MAQIS outperforms five counterpart approaches and our previous work. The effectiveness of MAQIS is also verified in the application of image processing.

关键词： membrane computing membrane algorithm Quantum-inspired evolutionary algorithm Knapsack problem Image sparse decomposition

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A Population-P-Systems-Inspired membrane algorithm for Multi-objective Optimization

A Population-P-Systems-Inspired Membrane Algorithm for Multi...

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9th International Conference on Bio-Inspired Computing - Theories and Applications (BIC-TA)

作者： Cheng, Jixiang Zhang, Gexiang Qin, Yanhui Southwest Jiaotong Univ Sch Elect Engn Chengdu 610031 Peoples R China State Grid Xinjiang Elect Power Corp Elect Power Res Inst Urumqi 830011 Peoples R China

ISBN: (纸本)9783662450482

This paper proposes a Population-P-Systems-inspired membrane algorithm (PPSMA) for multi-objective optimization. In the algorithm, the cells of population P systems are divided into two groups to implement different functions and the communications among cells are performed at two levels in order to obtain well converged and distributed solution set. Moreover, differential evolution is employed as search operator in PPSMA. Twelve multi-objective benchmark problems are utilized to test algorithm performance. Experimental results show that PPSMA performs better than five compared algorithms.

关键词： membrane algorithm Population P Systems Multi-objective Optimization

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Solving the Multiobjective Multiple Traveling Salesmen Problem Using membrane algorithm

Solving the Multiobjective Multiple Traveling Salesmen Probl...

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9th International Conference on Bio-Inspired Computing - Theories and Applications (BIC-TA)

作者： He, Juanjuan Wuhan Univ Sci & Technol Sch Comp Sci Wuhan 420081 Peoples R China

ISBN: (纸本)9783662450482

The multiple traveling salesmen problem (mTSP) is a generalization of the classical traveling salesman problem (TSP). The mTSP is more appropriate for real-life applications than the TSP, however, the mTSP has not received the same amount of attention. Due to the high complexity of the mTSP, a more efficient algorithm proposed for mTSP must be based on a global search procedure. membrane algorithms are a class of hybrid intelligence algorithms, which has been introduced recently as a global optimization technique. In this work, a new membrane algorithm for solving mTSP with different numbers of salesmen and problem sizes is described. The experiment results are compared with several multiobjective evolutionary strategies.

关键词： Evolutionary multiobjective optimization membrane algorithm Multiple traveling salesmen problem

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Improved membrane algorithm Under the Framework of P Systems to Solve Multimodal Multiobjective Problems

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2021年第7期35卷 2159024-2159024页

作者： Liu, Chuang Shen, Wanghui Zhang, Le Yang, Hong Du, Yingkui Yuan, Zhonghu Zhao, Hai Shenyang Univ Sch Informat Engn Shenyang 110044 Liaoning Peoples R China Northeastern Univ Sch Informat Sci & Engn Shenyang 110000 Peoples R China

Multimodal multiobjective problems (MMOPs) exist in scientific research and practical projects, and their Pareto solution sets correspond to the same Pareto front. Existing evolutionary algorithms often fall into local optima when solving such problems, which usually leads to insufficient search solutions and their uneven distribution in the Pareto front. In this work, an improved membrane algorithm is proposed for solving MMOPs, which is based on the framework of P system. More specifically, the proposed algorithm employs three elements from P system: object, reaction rule, and membrane structure. The object is implemented by real number coding and represents a candidate solution to the optimization problem to be solved. The function of the reaction rule of the proposed algorithm is similar to the evolution operation of the evolutionary algorithm. It can evolve the object to obtain a better candidate solution set. The membrane structure is the evolutionary logic of the proposed algorithm. It consists of several membranes, each of which is an independent evolutionary unit. This structure is used to maintain the diversity of objects, so that it provides multiple Pareto sets as output. The effectiveness verification study was carried out in simulation experiments. The simulation results show that compared with other experimental algorithms, the proposed algorithm has a competitive advantage in solving all 22 multimodal benchmark test problems in CEC2019.

关键词： Multimodal multiobjective optimization membrane algorithm chemical reaction optimization multiobjective optimization

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