Dynamic Behavior Analysis of Membrane-Inspired Evolutionary Algorithms

A hybrid path planning algorithm based on membrane pseudo-bacterial potential field (MemPBPF) is proposed. Membrane-inspired algorithms can reach an evolutionary behavior based on biochemical processes to find the best parameters for generating a feasible and safe path. The proposed MemPBPF algorithm uses a combination of the structure and rules of membrane computing. In that sense, the proposed MemPBPF algorithm contains dynamic membranes that include a pseudo-bacterial genetic algorithm for evolving the required parameters in the artificial potential field method. This hybridization between membrane computing, the pseudo-bacterial genetic algorithm, and the artificial potential field method provides an outperforming path planning algorithm for autonomous mobile robots. Computer simulation results demonstrate the effectiveness of the proposed MemPBPF algorithm in terms of path length considering collision avoidance and smoothness. Comparisons with two different versions employing a different number of elementary membranes and with other artificial potential field based algorithms are presented. The proposed MemPBPF algorithm yields improved performance in terms of time execution by using a parallel implementation on a multi-core computer. Therefore, the MemPBPF algorithm achieves high performance yielding competitive results for autonomous mobile robot navigation in complex and real scenarios. INDEX TERMS Artificial potential field, autonomous mobile robots, membrane computing, path planning, pseudo-bacterial genetic algorithm.

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“…These strengths and features are attractive and desirable for modeling complex problems considering a computational point of view [37].…”

Section: Membrane Computingmentioning

confidence: 99%

Hybrid Path Planning Algorithm Based on Membrane Pseudo-Bacterial Potential Field for Autonomous Mobile Robots

2019

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“…The combination of a P system framework with meta-heuristic algorithms 42 dates back to the year of 2004, when Nishida combined a nested membrane structure with a tabu search to solve traveling salesman problems. 51 Subsequently, this kind of approaches, called membrane-inspired evolutionary algorithms (MIEAs), 84,87 has gone through a fast development. In Ref.…”

Section: Introductionmentioning

confidence: 99%

“…This paper proposes a design strategy of a neural system that is capable of solving optimization problems. The proposed neural system is a P system that, unlike in MIEAs, 84,87 achieves the optimization results without the aid (in the optimization phase) of a metaheuristic. An ESNPS is developed by introducing the probabilistic selection of evolution rules and multi-neurons outputs and further a family of ESNPS are designed through introducing a guider to adaptively adjust rule probabilities to show how to use ESNPS to approximately solve a single objective and unconstrained combinatorial optimization problems.…”

Section: Introductionmentioning

confidence: 99%

An Optimization Spiking Neural P System for Approximately Solving Combinatorial Optimization Problems

Zhang

Rong

Neri

et al. 2014

Int. J. Neur. Syst.

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290

114

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Membrane systems (also called P systems) refer to the computing models abstracted from the structure and the functioning of the living cell as well as from the cooperation of cells in tissues, organs, and other populations of cells. Spiking neural P systems (SNPS) are a class of distributed and parallel computing models that incorporate the idea of spiking neurons into P systems. To attain the solution of optimization problems, P systems are used to properly organize evolutionary operators of heuristic approaches, which are named as membrane-inspired evolutionary algorithms (MIEAs). This paper proposes a novel way to design a P system for directly obtaining the approximate solutions of combinatorial optimization problems without the aid of evolutionary operators like in the case of MIEAs. To this aim, an extended spiking neural P system (ESNPS) has been proposed by introducing the probabilistic selection of evolution rules and multi-neurons output and a family of ESNPS, called optimization spiking neural P system (OSNPS), are further designed through introducing a guider to adaptively adjust rule probabilities to approximately solve combinatorial optimization problems. Extensive experiments on knapsack problems have been reported to experimentally prove the viability and effectiveness of the proposed neural system.

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“…[26], the relationship between different membrane structures and different communication topologies with respect to distributed evolutionary algorithms was analyzed; in Ref. [27], the dynamic behavior of the MIEA proposed in Ref. [12] was empirically investigated in terms of several diversity and convergence measures as well as the relationship between measures and fitness.…”

Section: Introductionmentioning

confidence: 99%

A Membrane-Inspired Evolutionary Algorithm Based on Population P Systems and Differential Evolution for Multi-Objective Optimization

Cheng¹,

Zhang²,

Wang³

2015

Jnl of Comp & Theo Nano

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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.

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Dynamic Behavior Analysis of Membrane-Inspired Evolutionary Algorithms

Cited by 31 publications

References 18 publications

Hybrid Path Planning Algorithm Based on Membrane Pseudo-Bacterial Potential Field for Autonomous Mobile Robots

Hybrid Path Planning Algorithm Based on Membrane Pseudo-Bacterial Potential Field for Autonomous Mobile Robots

An Optimization Spiking Neural P System for Approximately Solving Combinatorial Optimization Problems

A Membrane-Inspired Evolutionary Algorithm Based on Population P Systems and Differential Evolution for Multi-Objective Optimization

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