Nantiwat Pholdee

Comparative performance of meta-heuristic algorithms for mass minimisation of trusses with dynamic constraints

This paper investigates the search performances of various meta-heuristics (MHs) for solving truss mass minimisation with dynamic constraints. Several established MHs were used to solve five truss optimisation problems. The results obtained from using the various MHs were statistically compared based upon convergence rate and consistency. It was found that the best optimisers for this design task are evolution strategy with covariance matrix adaptation (CMAES) and differential evolution (DE). Furthermore, the best penalty function technique was discovered while four penalty function techniques assigned with several parameter settings were used in combination with the five best optimisers to solve the truss optimisation problems.

Hybridisation of real-code population-based incremental learning and differential evolution for multiobjective design of trusses

This paper proposes a hybrid evolutionary algorithm for multiobjective optimisation of trusses using real-code population-based incremental learning (RPBIL) to solve multiobjective design problems. Differential evolution (DE) operators are integrated into the main procedure of RPBIL leading to a hybrid algorithm. The newly developed optimiser, along with some established multiobjective evolutionary algorithms (MOEAs) is implemented to solve a number of multiobjective design problems of trusses. Comparative performance based upon a hypervolume indicator shows that the new hybrid multiobjective evolutionary algorithm is superior to the other MOEAs particularly in cases involving large-scale truss design problems.

Hybrid real-code population-based incremental learning and differential evolution for many-objective optimisation of an automotive floor-frame

In this paper, a many-objective hybrid real-code population-based incremental learning and differential evolution algorithm (MnRPBILDE) is proposed based on the concept of objective function space reduction. The method is then implemented on real engineering design problems. The topology, shape and sizing design of a simplified automotive floor-frame structure are formulated and used as test problems. A variety of well-established multi-objective evolutionary algorithms (MOEAs) including the original version of MnRPBILDE are employed to solve the test problems while the results are compared based on hypervolume and C indicators. The results indicate that our proposed algorithm outperforms the other MOEAs. The proposed algorithm is effective and efficient for many-objective optimisations of a car floor-frame structure.

Optimal Truss Sizing Using an Adaptive Differential Evolution Algorithm

AbstractTruss sizing optimization is one of the structural design problems that are most difficult to solve, since it may have a nonconvex feasible region in cases of statically indeterminate trusses with stress and displacement constraints. The successful use of metaheuristics to solve such a design problem, which has been found to be effective, has been studied for several decades. This paper presents a new metaheuristic for truss sizing design. The method is based on differential evolution concepts while a strategically adaptive scheme is employed. Also a new, simple, but efficient constraint handling technique is proposed so as to effectively deal with design constraints. Numerical tests show that the proposed optimizer is powerful and can be compared with the best performers found in the literature.

An efficient optimum Latin hypercube sampling technique based on sequencing optimisation using simulated annealing

This paper proposes a new optimal Latin hypercube sampling method (OLHS) for design of a computer experiment. The new method is based on solving sequencing and continuous optimisation using simulated annealing. There are two sets of design variables used in the optimisation process: sequencing and real number variables. The special mutation operator is developed to deal with such design variables. The performance of the proposed numerical strategy is tested and compared with three established OLHS methods, namely genetic algorithm (GA), enhanced stochastic evolutionary algorithm (ESEA) and successive local enumeration (SLE). Based on 30 test problems with various design dimensions and numbers of sampling points, the proposed method gives the best results. The method can generate an optimum set of sampling points within reasonable computing time; therefore, it can be considered as a powerful tool for design of computer experiments.

Efficient hybrid evolutionary algorithm for optimization of a strip coiling process

This article proposes an efficient metaheuristic based on hybridization of teaching–learning-based optimization and differential evolution for optimization to improve the flatness of a strip during a strip coiling process. Differential evolution operators were integrated into the teaching–learning-based optimization with a Latin hypercube sampling technique for generation of an initial population. The objective function was introduced to reduce axial inhomogeneity of the stress distribution and the maximum compressive stress calculated by Loves elastic solution within the thin strip, which may cause an irregular surface profile of the strip during the strip coiling process. The hybrid optimizer and several well-established evolutionary algorithms (EAs) were used to solve the optimization problem. The comparative studies show that the proposed hybrid algorithm outperformed other EAs in terms of convergence rate and consistency. It was found that the proposed hybrid approach was powerful for process optimization, especially with a large-scale design problem.

Optimal reactive power dispatch problem using a two-archive multi-objective grey wolf optimizer

Abstract In this paper, a novel two-archive Multi-Objective Grey Wolf Optimizer (2ArchMGWO) is proposed for solving Multi-Objective Optimal Reactive Power Dispatch (MORPD) problems. The optimizer has been improved from its original Multi-Objective Grey Wolf Optimizer (MGWO) by modifying the reproduction operator and adding the 2-archive concept to the algorithm. It is then implemented on solving MORPD with objective functions being active power loss minimization and voltage profile improvement (voltage deviation minimization). The generator bus voltages, tap setting transformers and shunt reactive power sources or flexible alternating current transmission systems are set as design variables. The proposed algorithm along with other existing multiobjective optimizers are applied to solve three test problems with the standard IEEE 30-bus, IEEE 57-bus, and the IEEE 118-bus power systems. The optimum results obtained from the various optimizers performance are compared based on the hypervolume indicator and they reveal that 2ArchMGWO is clearly superior to the others.

Hybrid real-code population-based incremental learning and approximate gradients for multi-objective truss design

In this article, real-code population-based incremental learning (RPBIL) is extended for multi-objective optimization. The optimizer search performance is then improved by integrating a mutation operator of evolution strategies and an approximate gradient into its computational procedure. RPBIL and its variants, along with a number of established multi-objective evolutionary algorithms, are then implemented to solve four multi-objective design problems of trusses. The design problems are posted to minimize structural mass and compliance while fulfilling stress constraints. The comparative results based on a hypervolume indicator show that the proposed hybrid RPBIL is the best performer for the large-scale truss design problems.

Surrogate-Assisted Evolutionary Optimizers for Multiobjective Design of a Torque Arm Structure

This paper presents two surrogate-assisted optimization strategies for structural constrained multiobjective optimization. The optimization strategies are based on hybridization of multiobjective population-based incremental learning (MOPBIL) and radial-basis function (RBF) interpolation. The first strategy uses MOPBIL for generating training points while the second strategy uses a Latin hypercube sampling (LHS) technique. The design case study is the shape and sizing design of a torque arm structure. A design problem is set to minimize structural mass and displacement while constraints include stresses due to three different load cases. Structural analysis is carried out by means of a finite element approach. The design problem is then tackled by the proposed surrogate-assisted design strategies. Numerical results show that the use of MOPBIL for generating training points is superior to the use of LHS based on a hypervolume indicator and root mean square error (RMSE).

Hybrid real-code ant colony optimisation for constrained mechanical design

This paper proposes a hybrid meta-heuristic based on integrating a local search simplex downhill (SDH) method into the search procedure of real-code ant colony optimisation (ACOR). This hybridisation leads to five hybrid algorithms where a Monte Carlo technique, a Latin hypercube sampling technique (LHS) and a translational propagation Latin hypercube design (TPLHD) algorithm are used to generate an initial population. Also, two numerical schemes for selecting an initial simplex are investigated. The original ACOR and its hybrid versions along with a variety of established meta-heuristics are implemented to solve 17 constrained test problems where a fuzzy set theory penalty function technique is used to handle design constraints. The comparative results show that the hybrid algorithms are the top performers. Using the TPLHD technique gives better results than the other sampling techniques. The hybrid optimisers are a powerful design tool for constrained mechanical design problems.