Pinkey Chauhan

Parameter optimization of multi-pass turning using chaotic PSO

Determination of an optimal set of machining parameters is needed to produce an ordered product of considerable quality and minimal manufacturing cost. The nonlinear and highly constrained nature of machining models restricts the application of classical gradient based techniques for handling such problems. The present study focuses on obtaining the optimal machining conditions during multi-pass turning operations. Methodology used is, a chaotic PSO namely Totally Disturbed Particle Swarm Optimization (TDPSO), an enhanced variant of PSO is employed for obtaining the optimal machining conditions during multi-pass turning operations subject to various constraints. In TDPSO, the phenomenon of chaos is embedded at different stages of PSO in order to make the search process more efficient. Results obtained by TDPSO are compared with results available in literature and it is observed that TDPSO is quite efficient for dealing with such problems.

Multi task selection including part mix, tool allocation and process plans in CNC machining centers using new binary PSO

This paper proposes a new binary PSO for solving multi-task selection problem concerning various issues such as Part mix, Tool allocation and Process plans in CNC machining centers. The mathematical formulation of considered selection problem emerges as highly constrained and 0-1, combinatorial optimization, which further belongs to the category of NP-hard problems. The proposed Binary PSO variant embeds a new sigmoid function namely “Gompertz function” as a binary number generator with an additional benefit of controlling its parameters so as to induce the combined effect of sigmoid as well as linear function. The corresponding variant is termed as “Gompertz Binary Particle Swarm Optimization (GBPSO)”. Before applying GBPSO for considered selection problem, the efficacy of proposed GBPSO is tested on a set of 0-1 Multi-dimensional knapsack problems and results are compared with standard binary PSO. Thereafter two test cases for considered optimal selection problem are solved and analyzed using GBPSO. The simulation results manifest the superiority of proposed variant over standard BPSO for solving benchmark problems and practical application as well.

Power Mutation Embedded Modified PSO for Global Optimization Problems

In the present study we propose a simple and modified framework for Particle Swarm Optimization (PSO) algorithm by incorporating in it a newly defined operator based on Power Mutation (PM). The resulting PSO variants are named as (Modified Power Mutation PSO) MPMPSO and MPMPSO 1 which differs from each other in the manner of implementation of mutation operator. In MPMPSO, PM is applied stochastically in conjugation with basic position update equation of PSO and in MPMPSO 1, PM is applied on the worst particle of swarm at each iteration. A suite of ten standard benchmark problems is employed to evaluate the performance of the proposed variations. Experimental results show that the proposed MPMPSO outperforms the existing method on most of the test functions in terms of convergence and solution quality.

Solving nonconvex trim loss problem using an efficient hybrid Particle Swarm Optimization

Trim loss is one of the most common problem arising in process industries. Its mathematical model is a nonconvex mixed integer nonlinear programming problem subject to several constraints. In this paper we consider four hypothetical cases, taken from literature [1] and propose an efficient approach based on Particle Swarm Optimization namely ILXPSO for solving trim loss problem. The numerical results when compared with the results available in literature [1] show the efficiency and robustness of the proposed algorithm.

Novel Binary PSO for Continuous Global Optimization Problems

The present study proposes a novel variant of binary PSO employing gompertz function for generating binary numbers with an additional benefit of controlling its parametrs so as to induce an combined effect of sigmoid as well as linear function. The corresponding variant is termend as Gompertz Binary Particle Swarm Optimization (GBPSO). The proposed GBPSO is tested on a a well known suite of 5 continuous benchmark fuctions. Numerical results indicate the competence of the proposed mechanism.

Totally disturbed chaotic Particle Swarm Optimization

Particle Swarm Optimization (PSO), classified as a swarm intelligence technique, mimics the well-informed swarming behavior of social species. A simple and effective searching strategy declares PSO as a potential member for solving various optimization problems. The present study embeds the concept of chaos at different stages of PSO, intending to enhance the convergence speed while trying to avoid stagnation and maintaining the solution quality. The proposed PSO variant is termed as “Totally disturbed PSO (TDPSO)”. The algorithm starts with a disturbed (chaotic) population, generated by considered chaotic system. Thereafter, when a certain number of iterations have elapsed and the searching process approaches equilibrium state, a relative velocity index is calculated for each particle to evaluate its present state and to decide whether or not the particle needs perturbation. The efficacy of proposed algorithm is tested against a set of benchmark problems and results are compared with existing Chaotic PSO and a standard PSO variant. Numerical results manifest that TDPSO works better over considered existing variants by effectively enhancing the searching capability and precision as well.

A new fine grained inertia weight Particle Swarm Optimization

Particle Swarm Optimization (PSO), analogous to behaviour of bird flocks and fish schools, has emerged as an efficient global optimizer for solving nonlinear and complex real world problems. The performance of PSO depends on its parameters to a great extent. Among all other parameters of PSO, Inertia weight is crucial one that affects the performance of PSO significantly and therefore needs a special attention to be chosen appropriately. This paper proposes an adaptive exponentially decreasing inertia weight that depends on particles performance iteration-wise and is different for each particle. The corresponding variant is termed as Fine Grained Inertia Weight PSO (FGIWPSO). The new inertia weight is proposed to improve the diversity of the swarm in order to avoid the stagnation phenomenon and a speeding convergence to global optima. The effectiveness of proposed approach is demonstrated by testing it on a suit of ten benchmark functions. The proposed FGIWPSO is compared with two existing PSO variants having nonlinear and exponential inertia weight strategies respectively. Experimental results assert that the proposed modification helps in improving PSO performance in terms of solution quality and convergence rate as well.

New Hybrid Discrete PSO for Solving Non Convex Trim Loss Problem

Trim loss minimization is the most common problem that arises during the cutting process, when products with variable width or length are to be produced in bulk to satisfy customer demands from limited available/stocked materials. The aim is to minimize inevitable waste material. Under various environmental and physical constraints, the trim loss problem is highly constrained, non convex, nonlinear, and with integer restriction on all variables. Due to the highly complex nature of trim loss problem, it is not easy for manufacturers to select an appropriate method that provides a global optimal solution, satisfying all restrictions. This paper proposes a discrete variant of PSO, which embeds a mutation operator, namely power mutation during the position update stage. The proposed variant is named as Hybrid Discrete PSO HDPSO. Binary variables in HDPSO are generated using sigmoid function with its domain derived from position update equation. Four examples with different levels of complexity are solved and results are compared with two recently developed GA and PSO variants. The computational studies indicate the competitiveness of proposed variant over other considered methods.

New Mutation Embedded Generalized Binary PSO

Particle Swarm Optimization (PSO) emerged as a potential global optimizer among other population based heuristics for solving continuous as well as discrete valued problems. This paper proposes a new modified binary PSO, which employs a generalized sigmoid function as a binary number generator with an additional benefit of controlling its only parameter to exploit the combined effect of sigmoid as well as linear function. Further, a logical mutation operator is also introduced to prevent stagnation of particles when algorithm does not show any improvement in objective function value for certain number of iterations. The proposed variant is termed as “Generalized Binary PSO with Mutation (GBPSOM)”. The local and global version of proposed variant are tested against a set of well-known benchmark functions and results are compared with corresponding versions of standard Binary PSO. Numerical results indicate the efficiency and reliability of proposed variant over standard version.