Modified particle swarm algorithm for scheduling agricultural products

Particle swarm optimization (PSO) algorithm is generally improved by adaptively adjusting the inertia weight or combining with other evolution algorithms. However, in most modified PSO algorithms, the random values are always generated by uniform distribution in the range of [0, 1]. In this study, the random values, which are generated by uniform distribution in the ranges of [0, 1] and [−1, 1], and Gauss distribution with mean 0 and variance 1 ( U [ 0 , 1 ] , U [ − 1 , 1 ] and G ( 0 , 1 ) ), are respectively used in the standard PSO and linear decreasing inertia weight (LDIW) PSO algorithms. For comparison, the deterministic PSO algorithm, in which the random values are set as 0.5, is also investigated in this study. Some benchmark functions and the pressure vessel design problem are selected to test these algorithms with different types of random values in three space dimensions (10, 30, and 100). The experimental results show that the standard PSO and LDIW-PSO algorithms with random values generated by U [ − 1 , 1 ] or G ( 0 , 1 ) are more likely to avoid falling into local optima and quickly obtain the global optima. This is because the large-scale random values can expand the range of particle velocity to make the particle more likely to escape from local optima and obtain the global optima. Although the random values generated by U [ − 1 , 1 ] or G ( 0 , 1 ) are beneficial to improve the global searching ability, the local searching ability for a low dimensional practical optimization problem may be decreased due to the finite particles.

Globalization and rapid technological changes have led to increased customer requirements and an intensified competition. To remain cost-efficient, manufacturing companies move from traditional manufacturing systems towards flexible manufacturing systems. Reconfigurable manufacturing systems have proven to be an effective way of adapting to the rapidly changing market conditions. Although, an increasing interest in this topic is visible in academics and industrial practice, the research is still limited. From an industrial point of view, a fast and easy adaptable solution for managing such a system dynamically is required. Based on an optimization model, a genetic algorithm for the dynamic management of cellular reconfigurable manufacturing systems was developed. A case study shows the effects of varying the selection method, the crossover operator, and the values for the occurrence of crossover and mutation processes. The application of the genetic algorithm resulted in an improvement of around 3% compared to the best solution of the initial population. Random selection showed the best results in the respective case. Nevertheless, it can be assumed that this selection method is outperformed by others as the number of generations increases.KeywordsReconfigurable manufacturingGenetic algorithmSelectionCrossoverMutationParameter variation

In the competitive shoe manufacturing industry, efficient production scheduling often struggles to balance minimizing makespan (MS) and reducing the number of cells, leading to increased complexity. This study introduces a biobjective optimization model to minimize the MS and the number of cells in a cellular manufacturing (CM) system. The model includes three phases: (1) a capacity phase calculates the required number of cells to meet the production demand within a suitable production rate; (2) a scheduling phase calculates the start and finish times for each product within a machine; (3) an optimization phase uses genetic algorithm (GA) for generating a set of optimal solutions considering the workforce distribution and product sequencing. The performance of the developed model was evaluated using a case study discussed in the literature to demonstrate its capabilities and highlight its superiority over similar models. These results showed a significant reduction of 0.4% in MS while maintaining the same number of cells compared to other models. A sensitivity analysis is conducted by varying the crossover and mutation probabilities. The findings indicate that the model achieves the most stable and consistent performance for paired crossover–mutation settings with crossover probabilities between 70% and 90% (corresponding to mutation probabilities between 30% and 10%), resulting in the lowest average MS and the most balanced cell utilization. The capabilities of the developed model provide production managers with reliable insights for identifying the production requirements of the shoe manufacturing company. The algorithm was also benchmarked on 12 published instances using 30 independent runs per instance to assess solution quality and stability.

SummaryThis paper presents the optimal designs of two analogue complementary metal–oxide–semiconductor (CMOS) amplifier circuits, namely differential amplifier with current mirror load and two‐stage operational amplifier. A modified Particle Swarm Optimization (PSO), called Craziness‐based Particle Swarm Optimization (CRPSO) technique is applied to minimize the total MOS area of the designed circuits. CRPSO is a highly modified version of conventional PSO, which adopts a number of random variables and has a better and faster exploration and exploitation capability in the multidimensional search space. Integration of craziness factor in the fundamental velocity term of PSO not only brings diversity in particles but also pledges convergence close to global best solution. The proposed CRPSO‐based circuit optimization technique is reassured to be free from the intrinsic disadvantages of premature convergence and stagnation, unlike Differential Evolution (DE), Harmony Search (HS), Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO). The simulation results achieved for the two analogue CMOS amplifier circuits establish the efficacy of the proposed CRPSO‐based approach over those of DE, HS, ABC and PSO in terms of convergence haste, design conditions and design goals. The optimally designed analogue CMOS amplifier circuits occupy the least MOS area and show the best performance parameters like gain and power dissipation, in compared with the other reported literature. Copyright © 2016 John Wiley & Sons, Ltd.

This article designs a joint decision model to solve cell formation and product scheduling problems together in cellular manufacturing systems. Multifunctional machines and multiskilled workers need to be grouped and assigned to work cells. Because of the latter's learning and forgetting effects, the production rate of each operation that requires the same machine function and is handled by the same pair of a machine and worker is different. Each product with an operation sequence is allowed to move from one machine to another for processing its subsequent operation, which may reduce product processing time at the expense of additional product movement time. In order to solve this intertwined optimization problem, an improved bacterial foraging algorithm (IBFA) is proposed to minimize makespan. In IBFA, a bacterium returns to the best position, once a worse tumble or swimming result occurs. Each bacterium can, thus, always go ahead toward favorable positions in the chemotactic procedure. In IBFA's reproduction strategy, half of the best bacteria are retained; while new bacteria are produced via crossover operations, and selectively retained. Through this method, high-quality and diversified population is produced. Moreover, in the reproduction and elimination-dispersal strategies of IBFA, the best bacterium can be retained to the subsequent generation through a population sorting method. Computational experiments and t-test are conducted to show that the proposed algorithm has the better performance than the classical one, genetic algorithm and two hybridized bacterial foraging algorithms given the same computational budget.

Particle Swarm Optimization (PSO) is a biologically inspired computational search and optimization method developed in 1995 by Eberhart and Kennedy based on the social behaviors of birds flocking or fish schooling.A number of basic variations have been developed due to improve speed of convergence and quality of solution found by the PSO.On the other hand, basic PSO is more appropriate to process static, simple optimization problem.Modification PSO is developed for solving the basic PSO problem.The observation and review 46 related studies in the period between 2002 and 2010 focusing on function of PSO, advantages and disadvantages of PSO, the basic variant of PSO, Modification of PSO and applications that have implemented using PSO.The application can show which one the modified or variant PSO that haven't been made and which one the modified or variant PSO that will be developed.

Particle swarm optimization (PSO) is one of the most famous nature-inspired algorithms, which has shown good performance on many optimization problems. To enhance the performance of PSO, this paper presents some modifications of PSO. The proposed approach is called MPSO, which employs a novel local search technique to obtain better candidate solutions. In order to verify the performance of the MPSO, we test it on ten well-known benchmark functions. Experimental results show that MPSO achieves better results than standard PSO and another improved PSO variant on the majority of test functions.

Since the last years different metaheuristic methods have been used to solve clustering problems. This paper addresses the problem of manufacturing Cell Formation using a modified particle swarm optimisation (PSO) algorithm. The main modification made to the original PSO algorithm consists on that in this work it is not used the vector of velocities as the standard PSO algorithm does. The proposed algorithm uses the concept of proportional likelihood with modifications, a technique that is used in data mining techniques. Some simulations are presented and compared. The criterion used to group the machines in cells is based on the minimization of inter-cell movements. The computational results show that the PSO algorithm is able to find the optimal solutions on almost all instances.

Collaborative particle swarm optimization with a data mining technique for manufacturing cell design

PurposeThis paper studies green vehicle routing problems of cold chain logistics with the consideration of the full set of greenhouse gas (GHG) emissions and an optimization model of green vehicle routing for cold chain logistics (with an acronym of GVRPCCL) is developed. The purpose of this paper is to minimize the total costs, which include vehicle operating cost, quality loss cost, product freshness cost, penalty cost, energy cost and GHG emissions cost. In addition, this research also investigates the effect of changing the vehicle maximum load in relation to cost and GHG emissions.Design/methodology/approachThis study develops a mathematical optimization model, considering the total cost and GHG emission. The standard particle swarm optimization and modified particle swarm optimization (MPSO), based on an intelligent optimization algorithm, are applied in this study to solve the routing problem of a real case.FindingsThe results of this study show the extend of the proposed MPSO performing better in achieving green-focussed vehicle routing and that considering the full set of GHG costs in the objective functions will reduce the total costs and environmental-diminishing emissions of GHG through the comparative analysis. The research outputs also evaluated the effect of different enterprises’ conditions (e.g. customers’ locations and demand patterns) for better distribution routes planning.Research limitations/implicationsThere are some limitations in the proposed model. This study assumes that the vehicle is at a constant speed and it does not consider uncertainties, such as weather conditions and road conditions.Originality/valuePrior studies, particularly in green cold chain logistics vehicle routing problem, are fairly limited. The prior works revolved around GHG emissions problem have not considered methane and nitrous oxides. This study takes into account the characteristics of cold chain logistics and the full set of GHGs.

Integration and diversity of IOT terminals and their applicable programs make them more vulnerable to many intrusive attacks. Thus, designing an intrusion detection model that ensures the security, integrity, and reliability of IOT is vital. Traditional intrusion detection technology has the disadvantages of low detection rates and weak scalability that cannot adapt to the complicated and changing environment of the Internet of Things. Hence, one of the most widely used traditional methods is the use of neural networks and also the use of evolutionary optimization algorithms to train neural networks can be an efficient and interesting method. Therefore, in this paper, we use the PSO algorithm to train the neural network and detect attacks and abnormalities of the IOT system. Although the PSO algorithm has many benefits, in some cases it may reduce population diversity, resulting in early convergence. Therefore,in order to solve this problem, we use the modified PSO algorithm with a new mutation operator, fuzzy systems and comparative equations. The proposed method was tested with CUP-KDD data set. The simulation results of the proposed model of this article show better performance and 99% detection accuracy in detecting different malicious attacks, such as DOS, R2L, U2R, and PROB.

Much has been reported in literature on virtual cell formation problems while a limited work is reported on worker assignments. Virtual Cellular Manufacturing Systems (VCMS) have come into existence, replacing traditional Cellular Manufacturing Systems (CMS), to meet highly dynamic production conditions in terms of demand, production lots, processing times, product mix and production sequences. Although the problem of worker assignment and flexibility in cell based manufacturing environments has been studied and analyzed in plenty using various heuristics/mathematical models, application of Artificial Neural Networks (ANN), adapted from the biological neural networks, is the recent development in this field exploiting the ability of ANN to work out mathematically-difficult-to-solve problems. In this attempt, the previous work of the author has been further developed and an attempt has been made to apply Learning Vector Quantization (LVQ) approach into worker assignment problems for higher order virtual cells i.e., three cells configurations and analyze the suitability of LVQ approach in terms of successful classification rate and simulation parameters for a number of VCMS periods.

Task allocation is one of the research focuses of multi-robot system. On the base of presenting the utility values matrix of robots relative to tasks and analyzing the characteristics of multi-robot task allocation, we build the multi-robot task allocation model based on robotic utility value. In order to prevent the basic particle swarm optimization (PSO) algorithm from converging on local optimum, this paper proposes a modified particle swarm optimization (MPSO) algorithm by introducing the linear decrease mechanism of inertia weight and the concept of adjustment operator and adjustment sequence. With the evolution of velocity in the MPSO algorithm, particle not only studies from the historical optimum individual of itself and population, but also studies from the other stochastic individuals with some probability. Finally, the MPSO algorithm is used to solve the task allocation problem of RoboCup 2D soccer robot system, the efficiency of this modified algorithm is proved through simulation results.

The paper proposes a modified particle swarm optimization algorithm (MPSO) for online tuning to the fuzzy controller of power system stabilizer (PSS). The hybrid fuzzy controller combines concepts from fuzzy systems and particle swarm optimization to produce powerful system controllers that are used for adaptive control. Two synchronous generators fitted with a PSS and are connected via double transmission lines will be the system under study. Three types of PSS controllers such as Conventional lead-lag PSS (CPSS), Static Fuzzy Logic Controller (SFLC) and online tuning Fuzzy Logic Controller based on PSO algorithm will be considered. The modified PSO algorithm used to adjust the fuzzy set parameters of each Fuzzy Logic Controller (FLC-PSO) online. The speed error and the change of error are the input vector, and the stabilizing signal is the output of both fuzzy logic controller (FLC). The FLC controllers will be tested under different disturbances for a wide-range of operating points. The computation time and the complexity of the fuzzy controller are reduced. The simulation results ensure the effectiveness of the proposed FLC-PSO controller for achieving the good dynamic response of the system under study.

This paper introduces a modified particle swarm algorithm to handle multiobjective optimization problems. In multiobjective PSO algorithms, the determination of Pareto optimal solutions depends directly on the strategy of assigning a best local guide to each particle. In this work, the PSO algorithm is modified to assign a best local guide to each particle by using minimum angular distance information. This algorithm is implemented to determine field-effect transistor (FET) model elements subject to the Pareto domination between the scattering parameters and operation bandwidth. Furthermore, the results are compared with those obtained by the nondominated sorting genetic algorithm-II. FET models are also built for the 3 points sampled from the different locations of the Pareto front, and a discussion is presented for the Pareto relation between the scattering parameter performances and the operation bandwidth for each model.