Memetic Particle Swarm Optimization Algorithm Research Articles

Simultaneous workload balancing and travel time minimization of automatic guided vehicles

This paper presents the application of a novel modified memetic particle swarm optimization algorithm (MMPSO) for simultaneous workload balancing and travel time minimization of automatic guided vehicles (AGVs) in the flexible manufacturing system (FMS). Three FMS layouts consisting of 5; 7 and 9 work centers are considered respectively for the simultaneous scheduling of AGVs in the FMS layouts. The resulting yield from the MMPSO algorithm is compared with the resulting yield of other implemented methods. From the results, it is observed that the application of the MMPSO algorithm outperforms the other applied algorithms from the literature. It was also observed that by the application of MMPSO algorithm a balanced exploration and exploitation of solutions can be achieved for the simultaneous scheduling of AGVs in the FMS.

Multi-load AGVs scheduling by application of modified memetic particle swarm optimization algorithm

The automated guided vehicles (AGVs) are extensively applied for material handling operations in the flexible manufacturing system (FMS) facilities. The scheduling decisions for the multi-load AGVs serving in the FMS with minimum travel time, waiting time and time to serve jobs are highly significant from the sustainable profits point of view. The present study proposes a combination of particle swarm optimization (PSO) for global search and memetic algorithm (MA) for local search termed as the modified memetic particle swarm optimization algorithm (MMPSO) for scheduling of multi-load AGVs in FMS. The newly proposed algorithm is applied for the generation of initial feasible solutions for scheduling of multi-load AGVs with minimum travel and minimum waiting time in the FMS. From the computational experiments, it is observed that the proposed MMPSO algorithm performs an effective and efficient exploration and exploitation process and further yields promising results for the multi-load AGVs scheduling problem in the FMS facility.

Scheduling of multi load AGVs in FMS by modified memetic particle swarm optimization algorithm

Scheduling of multi load AGVs in FMS by modified memetic particle swarm optimization algorithm

A memetic-based fuzzy support vector machine model and its application to license plate recognition

In this paper, a novel fuzzy support vector machine (FSVM) coupled with a memetic particle swarm optimization (MPSO) algorithm is introduced. Its application to a license plate recognition problem is studied comprehensively. The proposed recognition model comprises linear FSVM classifiers which are used to locate a two-character window of the license plate. A new MPSO algorithm which consists of three layers i.e. a global optimization layer, a component optimization layer, and a local optimization layer is constructed. During the construction process, MPSO performs FSVM parameters tuning, feature selection, and training instance selection simultaneously. A total of 220 real Malaysian car plate images are used for evaluation. The experimental results indicate the effectiveness of the proposed model for undertaking license plate recognition problems.

A Memetic Particle Swarm Optimization Algorithm for Community Detection in Complex Networks

In recent years, community detection has become a hot research topic in complex networks. Many of the proposed algorithms are for detecting community based on the modularity Q. However, there is a resolution limit problem in modularity optimization methods. In order to detect the community structure more effectively, a memetic particle swarm optimization algorithm (MPSOA) is proposed to optimize the modularity density by introducing particle swarm optimization-based global search operator and tabu local search operator, which is useful to keep a balance between diversity and convergence. For comparison purposes, two state-of-the-art algorithms, namely, meme-net and fast modularity, are carried on the synthetic networks and other four real-world network problems. The obtained experiment results show that the proposed MPSOA is an efficient heuristic approach for the community detection problems.

An adaptive memetic Particle Swarm Optimization algorithm for finding large-scale Latin hypercube designs

The Latin Hypercube (LH) design problem arises in computer simulations and is employed for examination and simulation of many physical events. In order to find a (near) optimal LH design, this paper proposes a new version of Particle Swarm Optimization (PSO) algorithm, which uses a population-based optimizer as the evolutionary part and a multiple-local-search procedure as the refinement part of the algorithm. To manage the problem constraints, the proposed algorithm utilizes a Ranked Ordered Value (ROV) rule, which converts the continuous space of solutions to the point-permutation space. Furthermore, to maintain the population diversity, the meta-Lamarckian learning strategy is applied to the local search procedure of the algorithm. In order to test the proposed algorithm, we compare it with a set of existing algorithms on several problem instances. To perform a fair comparison, the best parameters for all the algorithms are found and the experiments are performed based on these parameters. The experimental results show that the proposed algorithm outperforms the existing algorithms in solving large-scale LH design problem.

A memetic particle swarm optimization algorithm for solving the DNA fragment assembly problem

Determining the sequence of a long DNA chain first requires dividing it into subset fragments. The DNA fragment assembly (DFA) approach is then used for reassembling the fragments as an NP-hard problem that is the focus of increasing attention from combinatorial optimization researchers within the computational biology community. Particle swarm optimization (PSO) is one of the most important swarm intelligence meta-heuristic optimization techniques to solve NP-hard combinatorial optimization problems. This paper proposes a memetic PSO algorithm based on two initialization operators and the local search operator for solving the DFA problem by following the overlap---layout---consensus model to maximize the overlapping score measurement. The results, based on 19 coverage DNA fragment datasets, indicate that the PSO algorithm combining tabu search and simulated annealing-based variable neighborhood search local search can achieve the best overlap scores.

A memetic particle swarm optimization algorithm for multimodal optimization problems

Recently, multimodal optimization problems (MMOPs) have gained a lot of attention from the evolutionary algorithm (EA) community since many real-world applications are MMOPs and may require EAs to present multiple optimal solutions. In this paper, a memetic algorithm that hybridizes particle swarm optimization (PSO) with a local search (LS) technique, called memetic PSO (MPSO), is proposed for locating multiple global and local optimal solutions in the fitness landscape of MMOPs. Within the framework of the proposed MPSO algorithm, a local PSO model, where the particles adaptively form different species based on their indices in the population to search for different sub-regions in the fitness landscape in parallel, is used for globally rough exploration, and an adaptive LS method, which employs two different LS operators in a cooperative way, is proposed for locally refining exploitation. In addition, a triggered re-initialization scheme, where a species is re-initialized once converged, is introduced into the MPSO algorithm in order to enhance its performance of solving MMOPs. Based on a set of benchmark functions, experiments are carried out to investigate the performance of the MPSO algorithm in comparison with some EAs taken from the literature. The experimental results show the efficiency of the MPSO algorithm for solving MMOPs.