A novel chaotic Henry gas solubility optimization algorithm for solving real-world engineering problems

The paper proposes a novel metaheuristic based on integrating chaotic maps into a Henry gas solubility optimization algorithm (HGSO). The new algorithm is named chaotic Henry gas solubility optimization (CHGSO). The hybridization is aimed at enhancement of the convergence rate of the original Henry gas solubility optimizer for solving real-life engineering optimization problems. This hybridization provides a problem-independent optimization algorithm. The CHGSO performance is evaluated using various conventional constrained optimization problems, e.g., a welded beam problem and a cantilever beam problem. The performance of the CHGSO is investigated using both the manufacturing and diaphragm spring design problems taken from the automotive industry. The results obtained from using CHGSO for solving the various constrained test problems are compared with a number of established and newly invented metaheuristics, including an artificial bee colony algorithm, an ant colony algorithm, a cuckoo search algorithm, a salp swarm optimization algorithm, a grasshopper optimization algorithm, a mine blast algorithm, an ant lion optimizer, a gravitational search algorithm, a multi-verse optimizer, a Harris hawks optimization algorithm, and the original Henry gas solubility optimization algorithm. The results indicate that with selecting an appropriate chaotic map, the CHGSO is a robust optimization approach for obtaining the optimal variables in mechanical design and manufacturing optimization problems.

     

Multiobjective optimization of a steering linkage

In this paper, multi-objective optimization of a rack-and-pinion steering linkage is proposed. This steering linkage is a common mechanism used in small cars with three advantages as it is simple to construct, economical to manufacture, and compact and easy to operate. In the previous works, many researchers tried to minimize a steering error but minimization of a turning radius is somewhat ignored. As a result, a multi-objective optimization problem is assigned to simultaneously minimize a steering error and a turning radius. The design variables are linkage dimensions. The design problem is solved by the hybrid of multi-objective population-based incremental learning and differential evolution with various constraint handling schemes. The new design strategy leads to effective design of rack-and-pinion steering linkages satisfying both steering error and turning radius criteria.

     

A novel hybridized metaheuristic technique in enhancing the diagnosis of cross-sectional dent damaged offshore platform members

Offshore jacket platforms are widely used for oil and gas extraction as well as transportation in shallow to moderate water depth. Tubular cross-sectional elements are used to construct offshore platforms. Tubular cross sections impart higher resistance against hydrodynamic forces and have high torsional rigidity. During operation, the members can be partially or fully damaged due to lateral impacts. The lateral impacts can be due to ship collisions or through the impact of falling objects. The impact forces can weaken some members that influence the overall performance of the platform. This demonstrates an urgent need to develop a framework that can accurately forecast dent depth as well as dent angle of the affected members. This study investigates the use of an adaptive metaheuristics algorithm to provide automatic detection of denting damage in an offshore structure. The damage information includes dent depth and the dent angle. A model is developed in combination with the percentage of the dent depth of the damaged member and is used to assess the performance of the method. It demonstrates that small changes in stiffness of individual damaged bracing members are detectable from measurements of global structural motion.     

Multi-objective reliability-based topology optimization of structures using a fuzzy set model

This research proposes a multi-objective reliability-based topology optimization (MORBTO) for structural design, which considers uncertain structural parameters based on a fuzzy set model. The new technique is established in the form of multi-objective optimization where the equivalent possibilistic safety index (EPSI) is included as one of the objective functions along with mass, and compliance. This technique can reduce complexity due to a double-loop nest problem used previously due to performing single objective optimization. The present technique can accomplish within one optimization run using a multi-objective approach. Two design examples are used to demonstrate the present technique, which have the objectives as structural mass and compliance with the constraint of structural strength. The results show the proposed technique is effective and simple compared to previous techniques.

     

Optimum plate-fin heat sinks by using a multi-objective evolutionary algorithm

This article demonstrates the practical applications of a multi-objective evolutionary algorithm (MOEA) namely population-based incremental learning (PBIL) for an automated shape optimization of plate-fin heat sinks. The computational procedure of multi-objective PBIL is detailed. The design problem is posed to find heat sink shapes which minimize the junction temperature and fan pumping power while meeting predefined constraints. Three sets of shape design variables used in this study are defined as: vertical straight fins with fin height variation, oblique straight fins with steady fin heights, and oblique straight fins with fin height variation. The optimum results obtained from using the various sets of design variables are illustrated and compared. It can be said that, with this sophisticated design system, efficient and effective design of plate-fin heat sinks is achievable and the best design variables set is the oblique straight fins with fin height variation.     

Geometrical Design of Plate-Fin Heat Sinks Using Hybridization of MOEA and RSM

The work in this paper is aimed at demonstrating the practical multiobjective optimization of plate-fin heat sinks and the superiority of using a combined response surface method and multiobjective evolutionary optimizer over solely using the evolutionary optimizer. The design problem assigned is to minimize a heat sink junction temperature and fan pumping power. Design variables determine a heat sink geometry and inlet air velocity. Design constraints are given in such a way that the maximum and minimum fin heights are properly limited. Function evaluation is carried out by using finite volume analysis software. Two multiobjective evolutionary optimization strategies, real-code strength Pareto evolutionary algorithm with and without the use of a response surface technique, are implemented to explore the Pareto optimal front. The optimum results obtained from both design approaches are compared and discussed. It is illustrated that the multiobjective evolutionary technique is a powerful tool for the multiobjective design of electronic air-cooled heat sinks. With the same design conditions and an equal number of function evaluations, the multiobjective optimizer in association with the response surface technique totally outperforms the other. The design parameters affecting the diversity of the Pareto front include fin thickness, fin height distribution, and inlet air velocity while the plate base thickness and the total number of fins of the non-dominated solutions tend to approach certain values.     

Experiment and computation multi-fidelity multi-objective airfoil design optimization of fixed-wing UAV

Journal of Mechanical Science and Technology - Optimization with a surrogate model is a popular method used in design to avoid a high computational cost problem and is often encountered in...     

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 Love's 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.     

Simultaneous topology and sizing optimization of a water distribution network using a hybrid multiobjective evolutionary algorithm

This paper proposes a new direction for design optimization of a water distribution network (WDN). The new approach introduces an optimization process to the conceptual design stage of a WDN. The use of multiobjective evolutionary algorithms (MOEAs) for simultaneous topology and sizing design of piping networks is presented. The design problem includes both topological and sizing design variables while the objective functions are network cost and total head loss in pipes. The numerical technique, called a network repairing technique (NRT), is proposed to overcome difficulties in operating MOEAs for network topological design. The problem is then solved by using a number of established and newly developed MOEAs. Also, two new MOEAs namely multiobjective real code population-based incremental learning (RPBIL) and a hybrid algorithm of RPBIL with differential evolution (termed RPBIL–DE) are proposed to tackle the design problems. The optimum results obtained are illustrated and compared. It is shown that the proposed network repairing technique is an efficient and effective tool for topological design of WDNs. Based on the hypervolume indicator, the proposed RPBIL–DE is among the best MOEA performers.     

A novel self-adaptive hybrid multi-objective meta-heuristic for reliability design of trusses with simultaneous topology,shape and sizing optimisation design variables

Structural and Multidisciplinary Optimization - Performing the design of a truss including topological, shape and sizing (TSS) variables simultaneously is a challenging but important task for a...