Minimum weight design of trusses using improved move limit method of sequential linear programming

John, Keerthana; Ramakrishnan, C.; Sharma, Kalpana

doi:10.1016/0045-7949(87)90073-3

Cited by 33 publications

(5 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are some methods based on mathematical programming to dispose the discrete design variables [49]. But they may fall into local optima, and the anticipated optimization results cannot be obtained.…”

Section: Apsoc For Constraint Optimization Problem With Discrete Varimentioning

confidence: 99%

“…This method is also selected to address the problem with discrete variables. If each element of the vector X belongs to S d which is a scalar set {S 1 , S 2 , …, S p }, then the optimization model (5) has the constraints with discrete variables [49][50][51][52][53][54][55] which is a combination optimization problem with its best solution from all possible variable combinations [53].…”

Section: Apsoc For Constraint Optimization Problem With Discrete Varimentioning

confidence: 99%

See 1 more Smart Citation

Computational efficiency of accelerated particle swarm optimization combined with different chaotic maps for global optimization

Yang

Liu

2016

Neural Comput & Applic

View full text Add to dashboard Cite

A new hybrid chaos optimization algorithm (COA), namely, the accelerated particle swarm optimization combined with different chaotic maps (APSOC), is proposed for global optimization with continuous and discrete variables in this paper. Computational efficiency of APSOC and other three COAs (CPSO1-CPSO3) is compared for nonlinear benchmark functions. And the three influencing factors of chaotic maps on efficiency are considered, namely, the Lyapunov exponent (LE) which quantifies the search speed of chaotic sequence, the probability distribution function (PDF), and the dispersion degree of chaotic sequence which is defined as an index to measure the computational performance of evolutionary algorithm herein. To investigate the influence of CPSOs with different one-dimensional chaotic maps on efficiency of global optimization, three cases are examined, such as: different chaotic maps with close LE and different PDF; the same chaotic map with the same PDF and different LE; and the identical chaotic map with equal or close LE and different PDF. Optimization results demonstrate that the probability distribution, search speed, and dispersion degree of chaotic sequences affect remarkably the performance of CPSOs. Finally, statistic results and evolution curves of APSOC with Circle map are compared with those of other three COAs, and the optimal design of trusses with discrete variables are performed by APSOC. It is indicated that APSOC with Circle map is superior to other CPSOs and has greater exploration ability and faster convergence rate.

show abstract

Section: Apsoc For Constraint Optimization Problem With Discrete Varimentioning

confidence: 99%

Section: Apsoc For Constraint Optimization Problem With Discrete Varimentioning

confidence: 99%

Computational efficiency of accelerated particle swarm optimization combined with different chaotic maps for global optimization

Yang

Liu

2016

Neural Comput & Applic

View full text Add to dashboard Cite

show abstract

“…The cantilever truss shown in Fig. 1 with 10 independent design variables has been studied by many researchers Schmit and Farshi [12], Schmit and Miura [13],Venkayya [14], Sedaghati [15], Kaveh and Rahami [16], Li et al [17], Farshi and Ziazi [17], Rizzi [18], John et al [19]. A material density of 0.1lb/(in 3 ) and a modulus of elasticity of 10000 ksi is commonly used.…”

Section: The 10-bar Trussmentioning

confidence: 99%

“…Variables(in 2 )Schmit and Farsh[12] Schmit and Miura[13] Venkayya[14] Rizzi[18] John et al[19] Lie at al [17]. Farshi and Ziazi[21] This work 2 = 6.425cm 2 1lb = 4.448N…”

mentioning

confidence: 91%

Sizing Optimization of Truss Structures using a Hybridized Genetic Algorithm

Asl,

Aslani,

Panahi

2013

Preprint

View full text Add to dashboard Cite

This paper presents a genetic-based hybrid algorithm that combines the exploration power of Genetic Algorithm (GA) with the exploitation capacity of a phenotypical probabilistic local search algorithm. Though not limited to a certain class of optimization problems, the proposed algorithm has been "fine tuned" to work particularly efficiently on the optimal design of planar and space structures, a class of problems characterized by the large number of design variables and constraints, high degree of non-linearity and multitude of local minima. The proposed algorithm has been applied to the skeletal weight reduction of various planar and spatial trusses and shown to be superior in all of the cases.

show abstract

“…Most attempts to utilize SLP report convergence issues necessitating the ad hoc establishment of moving limits and were not that efficient (Bhaviktti and Ramakrishnan, ; John et al., ; Chen, ). For example, Chen () proposed six methods for establishing moving limits, which complicates the procedure significantly.…”

Section: Introductionmentioning

confidence: 99%

Solving nonconvex nonlinear programs with reverse convex constraints by sequential linear programming

Drezner

Kalczyński

2019

Int Trans Operational Res

View full text Add to dashboard Cite

The sequential linear programming (SLP) method for solving nonlinear problems was introduced in the 1960s. Many papers that attempted to use SLP reported poor performance and convergence issues. We found that nonlinear programs with reverse convex constraints, which are the most difficult nonlinear programs with many local optima, are solved (heuristically) very well by SLP. We proved that for this type of problems, the solutions to the sequence of the linear programming problems converge to a local optimum. Since the final solution depends on the starting solution, we propose to apply SLP in a multistart approach starting from randomly generated solutions. This multistart SLP is very easy to implement. We recommend that the research community reconsiders the application of SLP for this type of problems.

show abstract

Minimum weight design of trusses using improved move limit method of sequential linear programming

Cited by 33 publications

References 5 publications

Computational efficiency of accelerated particle swarm optimization combined with different chaotic maps for global optimization

Computational efficiency of accelerated particle swarm optimization combined with different chaotic maps for global optimization

Sizing Optimization of Truss Structures using a Hybridized Genetic Algorithm

Solving nonconvex nonlinear programs with reverse convex constraints by sequential linear programming

Contact Info

Product

Resources

About