Hybrid non-parametric particle swarm optimization and its stability analysis

Liu, Zhao-Guang; Ji, Xuanrong

doi:10.1016/j.eswa.2017.09.012

Cited by 16 publications

(7 citation statements)

References 52 publications

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“…The most promising method for solving these sorts of problems are dedicated search algorithms based on random strategies [58][59][60] and the projection of the informational bases of biological systems onto computing technologies. The most popular are currently ANN and genetic algorithms; both methods are effective in the field of local strategies and, most importantly, both provide the opportunity of exiting these fields during a global search.…”

Section: Process Operation Designmentioning

confidence: 99%

Artificial Intelligence Monitoring of Hardening Methods and Cutting Conditions and Their Effects on Surface Roughness, Performance, and Finish Turning Costs of Solid-State Recycled Aluminum Alloy 6061 Сhips

Abbas

Pimenov

Ердаков

et al. 2018

Metals

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Aluminum Alloy 6061 components are frequently manufactured for various industries-aeronautics, yachting, and optical instruments-due to their excellent physical and mechanical properties, including corrosion resistance. There is little research on the mechanical tooling of AA6061 and none on its structure and properties and their effects on surface roughness after finish turning. The objective of this comprehensive study is, therefore, to ascertain the effects of both the modern method of hardening AA6061 shafts and the finish turning conditions on surface roughness, Ra, and the minimum machining time for unit-volume removal, T m , while also establishing the cost price of processing one part, C. The hardening methods improved both the physical and the mechanical material properties processed with 2, 4, and 6 passes of equal channel angular pressing (ECAP) at room temperature, using an ECAP-matrix with a channel angle of 90 •. The reference workpiece sample was a hot extruded chip under an extrusion ratio (ER) of 5.2 at an extrusion temperature of 500 • C (ET = 500 • C). The following results were obtained: grain size in ECAP-6 decreased from 15.9 to 2.46 µm, increasing both microhardness from 41 Vickers hardness value (HV) to 110 HV and ultimate tensile strength from 132.4 to 403 MPa. The largest decrease in surface roughness, Ra-70%, was obtained turning a workpiece treated with ECAP-6. The multicriteria optimization was computed in a multilayer perceptron-based artificial neural network that yielded the following optimum values: the minimal length of the three-dimensional estimates vector with the coordinates Ra = 0.800 µm, T m = 0.341 min/cm 3 , and C = 6.955 $ corresponded to the optimal finish turning conditions: cutting speed v c = 200 m/min, depth of cut a p = 0.2 mm, and feed per revolution f r = 0.103 mm/rev (ET-500 extrusion without hardening).

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Section: Process Operation Designmentioning

confidence: 99%

Artificial Intelligence Monitoring of Hardening Methods and Cutting Conditions and Their Effects on Surface Roughness, Performance, and Finish Turning Costs of Solid-State Recycled Aluminum Alloy 6061 Сhips

Abbas

Pimenov

Ердаков

et al. 2018

Metals

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show abstract

“…Aminbakhsh and Sonmez [17] studied the discrete time-cost trade-off problem by developing a discrete PSO based on the principles for representation, initialization, and position updating of the particles. Liu et al [18] proposed a hybrid non-parametric PSO algorithm for selecting suitable parameters. Operations, including a multi-crossover operation, a vertical crossover, and an exemplar-based learning strategy, were combined to improve the global and the local exploration capabilities.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Equation (15) shows that only one vehicle can travel to shipment point j (excluding the factory, i.e., j = 0) in period t. Equation (16) makes sure that at most, one vehicle can travel from shipment point i (excluding the factory, i.e., i = 0) to one single shipment point in period t. Equation (17) ensures that each vehicle, if dispatched, can only travel starting from the factory (i = 0) in each period. Equation (18) shows that all of the vehicles that travel from the factory (i = 0) will go back to the factory in each period. Equation (19) ensures that if vehicle e travels from shipment point i (excluding the factory, i.e., i = 0) it will travel to only one shipment point j directly in each period.…”

Section: Mixed Integer Programming (Mip)mentioning

confidence: 99%

An Integrated Approach for Sustainable Supply Chain Management with Replenishment, Transportation, and Production Decisions

Lee

Kang

et al. 2018

Sustainability

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Sustainable supply chain management is important for most firms in today’s competitive environment. This study considers a supply chain environment under which the firm needs to make decisions regarding from which supplier and what quantity of parts should be purchased, which vehicle with a certain emissions amount and transportation capacity should be assigned, and what kind of production mode should be used. The integrated replenishment, transportation, and production problem is concerned with coordinating replenishment, transportation, and production operations to meet customer demand with the objective of minimizing the cost. The problem considered in this study involves heterogeneous vehicles with different emission costs, various materials with dissimilar emission costs, and distinct production modes, each with their own emission costs. In addition, multiple suppliers with different quantity discount schemes are considered, different kinds of vehicles with different loading capacities and traveling distance limits are present, and different production modes with different production capacities and production costs are included. A mixed integer programming model is proposed first to minimize the total cost, which includes the ordering cost, purchase cost, transportation cost, emission cost, production cost, inventory-holding cost, and backlogging cost, while satisfying various constraints in replenishment, transportation, and production. A particle swarm optimization model is constructed next to deal with large-scale problems that are too complicated to solve by the mixed integer programming. The main advantage of the proposed models lies in their ability to simultaneously coordinate the replenishment, transportation, and production operations in a planning horizon. The proposed particle swarm optimization model could further identify a near-optimal solution to the complex problem in a very short computational time. To the best of the authors’ knowledge, this is the first paper that considers the sustainable supply chain management problem with multiple suppliers, multiple vehicles, and multiple production modes simultaneously. Case studies are presented to examine the practicality of the mixed integer programming and the particle swarm optimization models. The proposed models can be adopted by the management to make relevant supply chain management decisions.

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“…In (ii), the parameters inertia weight [31], [61], acceleration coefficients [12], [27], and random numbers [8], [51] attract much more attention and have become focus of research in the area of PSO in recent years. In (iii), there is no need to tune any algorithmic parameter in PSO by removing all the parameters from the standard particle swarm optimization [6], [29], [37]. In (iv), the whole swarm in PSO can be divided into several sub-swarms during the search process so as to explore different sub-regions of the solution space with different search strategies [11], [20], [35], [70].…”

Section: Introductionmentioning

confidence: 99%

DMPSO: Diversity-Guided Multi-Mutation Particle Swarm Optimizer

2019

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Particle swarm optimization (PSO) is a population based meta-heuristic search technique that has been widely applied to deal with various optimization problems. However, like other stochastic methods, PSO also encounters the problems of entrapment into local optima and premature convergence in solving complex multimodal problems. To tackle these issues, a diversity-guided multi-mutation particle swarm optimizer (abbreviated as DMPSO) is presented in this paper. To start with, the chaos opposition-based learning (OBL) is employed to yield high-quality initial particles to accelerate the convergence speed of DMPSO. Followed by, the self-regulating inertia weight is leveraged to strike a balance between the exploration and exploitation in the search space. After that, three different kinds of mutation strategies (gaussian, cauchy and chaotic mutations) are used to maintain the potential diversity of the whole swarm based on an effective diversity-guided mechanism. In particular, an auxiliary velocity-position update mechanism is exclusively applied to the global best particle that can effectively guarantee the convergence of the DMPSO. Finally, extensive experiments on a set of well-known unimodal and multimodal benchmark functions demonstrate that DMPSO outperforms most of the other tested PSO variants in terms of both the solution quality and its efficiency.

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Hybrid non-parametric particle swarm optimization and its stability analysis

Cited by 16 publications

References 52 publications

Artificial Intelligence Monitoring of Hardening Methods and Cutting Conditions and Their Effects on Surface Roughness, Performance, and Finish Turning Costs of Solid-State Recycled Aluminum Alloy 6061 Сhips

Artificial Intelligence Monitoring of Hardening Methods and Cutting Conditions and Their Effects on Surface Roughness, Performance, and Finish Turning Costs of Solid-State Recycled Aluminum Alloy 6061 Сhips

An Integrated Approach for Sustainable Supply Chain Management with Replenishment, Transportation, and Production Decisions

DMPSO: Diversity-Guided Multi-Mutation Particle Swarm Optimizer

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