A multi-objective particle swarm for constraint and unconstrained problems

Nshimirimana, Robert; Abraham, Ajith; Nothnagel, G.

doi:10.1007/s00521-020-05555-6

Cited by 17 publications

(8 citation statements)

References 81 publications

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“…After selecting guides, each particle's velocity and position is updated according to Equations ( 6) and ( 5). The MOPSO algorithm [51,52] implemented in this study is briefly outlined in Figure 2.…”

Section: Multi-objective Particle Swarm Optimization (Mopso)mentioning

confidence: 99%

A Soft Computing-Based Analysis of Cutting Rate and Recast Layer Thickness for AZ31 Alloy on WEDM Using RSM-MOPSO

et al. 2022

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In the present research, the AZ31 alloy is machined by wire-cut electric discharge machining (WEDM). The experiments were designed according to the Box-Behnken design (BBD) of response surface methodology (RSM). The input process variables, namely servo feed (SF), pulse on-time (Ton), servo voltage (SV), and pulse off-time (Toff), were planned by BBD, and experiments were performed to investigate the cutting rate (CR) and recast layer thickness (RCL). The analysis of variance (ANOVA) was performed to determine the influence of machining variables on response characteristics. The empirical models developed for CR and RCL were solved using Multi-Objective Particle Swarm Optimization (MOPSO). Pareto optimal front is used for the collective optimization of CR and RCL. The optimal solution suggested by the hybrid approach of RSM-MOPSO is further verified using a confirmation test on the random setting indicated by the hybrid algorithm. It is found that the minimum RCL (6.34 µm) is obtained at SF: 1700; SV: 51 V; Toff: 10.5 µs; and Ton: 0.5 µs. However, maximum CR (3.18 m/min) is predicted at SF: 1900; SV: 40 V; Toff: 7 µs; and Ton: 0.9 µs. The error percentage of ±5.3% between the experimental results and predicted solutions confirms the suitability of the proposed hybrid approach for WEDM of AZ31.

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Section: Multi-objective Particle Swarm Optimization (Mopso)mentioning

confidence: 99%

A Soft Computing-Based Analysis of Cutting Rate and Recast Layer Thickness for AZ31 Alloy on WEDM Using RSM-MOPSO

et al. 2022

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“…Various improved implementations for the PSO algorithm have been presented [2, 3,[16][17][18][19]. For instance, an improved version of the PSO algorithm with an adaptive learning strategy called PSO-ALS was presented in [17] in order to improve the population diversity and augment the global search strength.…”

Section: Related Workmentioning

confidence: 99%

Heterogeneous Parallel Implementation of PSO Algorithm with an Aging Leader and Challengers

Yazdanpanah

2022

Preprint

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Parallel computing techniques provide high-performance execution of large-size and real-world complicated problems such as heuristic optimization algorithms. One of the most popular optimization algorithms is PSO (particle swarm optimization) which is an intelligent search method for finding the best solution according to population state. The PSO algorithm has obtained significant attention from researchers and different parallel implementations of this algorithm have been presented for intensive-computing applications. The ALC-PSO algorithm (PSO with an aging leader and challengers) is an improved population-based and intensive-computation procedure thanks to the high fitness analyses. Compared to the traditional PSO, the ALC-PSO algorithm improves convergence rapidity. In this paper, we have developed a novel heterogeneous parallel implementation of the ALC-PSO algorithm using OmpSs and CUDA, for execution on both CPU and GPU cores. OmpSs is a task-based parallel programming model that uses runtime system and hardware for automatic exploiting and managing parallelisms. Combination of OmpSs with CUDA helps developers accelerate their applications running on both CPUs and GPUs. The results demonstrate that the proposed approach (i.e., the OmpSs-CUDA implementation of the ALC-PSO algorithm) provides higher performance than the serial, and also the CUDA-based parallel implementations of the ALC-PSO algorithm.

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“…If PSO parameters are not properly set, then there are chances that it can get trapped in local optimum due to lack of local exploitation, global exploration and diversity issues in the search space [19]. To solve the combinatorial optimization problems, multiple modified PSO variants are proposed in [20,21], such as multi-objective optimization [22], constraint optimization [23], opposition-based variant [24], adaptive inertia weight [25] and mutation operator [26]. In addition, many modifications have been made to the PSO algorithm to improve its convergence.…”

Section: Introductionmentioning

confidence: 99%

An Improved Particle Swarm Optimization Algorithm for Data Classification

Bangyal¹,

Nisar

Soomro

et al. 2022

Applied Sciences

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Optimisation-based methods are enormously used in the field of data classification. Particle Swarm Optimization (PSO) is a metaheuristic algorithm based on swarm intelligence, widely used to solve global optimisation problems throughout the real world. The main problem PSO faces is premature convergence due to lack of diversity, and it is usually stuck in local minima when dealing with complex real-world problems. In meta-heuristic algorithms, population initialisation is an important factor affecting population diversity and convergence speed. In this study, we propose an improved PSO algorithm variant that enhances convergence speed and population diversity by applying pseudo-random sequences and opposite rank inertia weights instead of using random distributions for initialisation. This paper also presents a novel initialisation population method using a quasi-random sequence (Faure) to create the initialisation of the swarm, and through the opposition-based method, an opposite swarm is generated. We proposed an opposition rank-based inertia weight approach to adjust the inertia weights of particles to increase the performance of the standard PSO. The proposed algorithm (ORIW-PSO-F) has been tested to optimise the weight of the feed-forward neural network for fifteen data sets taken from UCI. The proposed techniques’ experiment result depicts much better performance than other existing techniques.

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A multi-objective particle swarm for constraint and unconstrained problems

Cited by 17 publications

References 81 publications

A Soft Computing-Based Analysis of Cutting Rate and Recast Layer Thickness for AZ31 Alloy on WEDM Using RSM-MOPSO

A Soft Computing-Based Analysis of Cutting Rate and Recast Layer Thickness for AZ31 Alloy on WEDM Using RSM-MOPSO

Heterogeneous Parallel Implementation of PSO Algorithm with an Aging Leader and Challengers

An Improved Particle Swarm Optimization Algorithm for Data Classification

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