This paper proposes a membrane-inspired evolutionary algorithm based on population P systems and differential evolution for multi-objective optimization. In the algorithm, the cells of population P systems are divided...
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This paper proposes a membrane-inspired evolutionary algorithm based on population P systems and differential evolution for multi-objective optimization. In the algorithm, the cells of population P systems are divided into two groups. The first group, consisting of most of cells, focuses on evolving objects using differential evolution rules while the second group, consisting of only one cell, aims at selecting and re-distributing objects across the first group of cells for next generation using a special selection rule. Moreover, the communications among cells are performed at both local and global levels in order to obtain well converged and distributed solution set. Twelve benchmark problems with diverse features are utilized to test algorithm performance. Experimental results show that the proposed approach outperforms five well-known algorithms in terms of three performance metrics.
This paper proposes a Populationmembrane-system-inspiredevolutionaryalgorithm(PMSIEA), which is designed by using a population P system and a Quantum-inspiredevolutionaryalgorithm(QIEA). PMSIEA uses the population...
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This paper proposes a Populationmembrane-system-inspiredevolutionaryalgorithm(PMSIEA), which is designed by using a population P system and a Quantum-inspiredevolutionaryalgorithm(QIEA). PMSIEA uses the population P system with three cells to organize three variants of QIEAs, where communications between cells are performed at the level of genes,instead of the level of individuals reported in the existing membranealgorithms. This work provides a useful framework for synthesizing different algorithms at macro level and exchanging genic information at micro scale. Experimental results conduced on knapsack problems show that PMSIEA is superior to four representative QIEAs and our previous work with respect to the quality of solutions and the elapsed time. We also use PMSIEA to solve the optimal distribution system reconfiguration problem in power systems for minimizing the power loss.
In recent years, many different membrane-inspired evolutionary algorithms have been proposed to solve various complex optimization problems. Considering membrane systems' powerful computing performance and paralle...
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In recent years, many different membrane-inspired evolutionary algorithms have been proposed to solve various complex optimization problems. Considering membrane systems' powerful computing performance and parallel capability, it has outstanding potential in solving multi-task optimization problems. However, there is no research to explore the performance of membrane-inspired evolutionary algorithms in solving multi-task optimization problems. In this paper, a novel membrane-inspiredevolutionary framework with a hybrid dynamic membrane structure is proposed to solve the multi-objective multi-task optimization problems. First, a novel membrane-inspired two-stage evolution strategy algorithm is proposed as the algorithm in the membrane to improve the convergence of the algorithm and the diversity of multisets. Second, the information molecule concentration vector is proposed to reduce negative information transfer. The information molecule concentration vector is inspired by the binding process of information molecules and receptors and can control the information transfer probability adaptively. Finally, comprehensive experimental results show that the proposed algorithm performs better than most advanced multi-objective evolutionary multitasking algorithms. (c) 2022 Elsevier Inc. All rights reserved.
In this paper, a membraneevolutionary artificial potential field (memEAPF) approach for solving the mobile robot path planning problem is proposed, which combines membrane computing with a genetic algorithm (membrane...
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In this paper, a membraneevolutionary artificial potential field (memEAPF) approach for solving the mobile robot path planning problem is proposed, which combines membrane computing with a genetic algorithm (membrane-inspired evolutionary algorithm with one-level membrane structure) and the artificial potential field method to find the parameters to generate a feasible and safe path. The memEAPF proposal consists of delimited compartments where multisets of parameters evolve according to rules of biochemical inspiration to minimize the path length. The proposed approach is compared with artificial potential field based path planning methods concerning to their planning performance on a set of twelve benchmark test environments, and it exhibits a better performance regarding path length. Experiments to demonstrate the statistical significance of the improvements achieved by the proposed approach in static and dynamic environments are shown. Moreover, the implementation results using parallel architectures proved the effectiveness and practicality of the proposal to obtain solutions in considerably less time. (C) 2019 Elsevier B.V. All rights reserved.
evolutionarymembrane computing is an important research direction of membrane computing that aims to explore the complex interactions between membrane computing and evolutionary computation. These disciplines are rec...
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evolutionarymembrane computing is an important research direction of membrane computing that aims to explore the complex interactions between membrane computing and evolutionary computation. These disciplines are receiving increasing attention. In this paper, an overview of the evolutionarymembrane computing state-of-the-art and new results on two established topics in well defined scopes (membrane-inspired evolutionary algorithms and automated design of membrane computing models) are presented. We survey their theoretical developments and applications, sketch the differences between them, and compare the advantages and limitations. (C) 2014 Elsevier Inc. All rights reserved.
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