Real Engineering Design Problems Research Articles

CMGWO: Grey wolf optimizer for fusion cell-like P systems

The grey wolf optimizer is a widely used parametric optimization algorithm. It is affected by the structure and rank of grey wolves and is prone to falling into the local optimum. In this study, we propose a grey wolf optimizer for fusion cell-like P systems. Cell-like P systems can parallelize computation and communicate from cell membrane to cell membrane, which can help the grey wolf optimizer jump out of the local optimum. Design new convergence factors and use different convergence factors in other cell membranes to balance the overall exploration and utilization capabilities of the algorithm. At the same time, dynamic weights are introduced to accelerate the convergence speed of the algorithm. Experiments are performed on 24 test functions to verify their global optimization performance. Meanwhile, a support vector machine model optimized by the grey wolf optimizer for fusion cell-like P systems has been developed and tested on six benchmark datasets. Finally, the optimizing ability of grey wolf optimizer for fusion cell-like P systems on constrained optimization problems is verified on three real engineering design problems. Compared with other algorithms, grey wolf optimizer for fusion cell-like P systems obtains higher accuracy and faster convergence speed on the test function, and at the same time, it can find a better parameter set stably for the optimization of support vector machine parameters, in addition to being more competitive on constrained engineering design problems. The results show that grey wolf optimizer for fusion cell-like P systems improves the searching ability of the population, has a better ability to jump out of the local optimum, has a faster convergence speed, and has better stability.

A step-size follow-the-leader optimization algorithm with an improved step parameters

Follow the leader (FTL) algorithm is a newly developed optimization algorithm inspired by a sheep’s movement within a flock. FTL has been successfully implemented to solve power prediction problems. However, the probability of falling in local optima is high due to randomness in the step parameter. This paper proposes a step-size follow-the-leader (SFTL) algorithm with decreasing and increasing step-size combinations. The improved step parameter tunes the search space by generating a new solution to improve the accuracy and convergence rate of the FTL algorithm. Four different FTL variants have been presented in this paper to show the impact of the dynamic step-size parameter. FTL improvement is verified by testing SFTL over thirty-two fixed unimodal, unimodal, fixed multimodal, and multimodal benchmark functions. The computational results indicate that SFTL significantly improves the basic FTL algorithm and converges early compared to other algorithms. SFTL has also been tested on five real engineering design problems and obtained results show that SFTL outperformed FTL and other popular optimization algorithms.

Termite life cycle optimizer

This paper introduces a novel bio-inspired meta-heuristic optimization algorithm, named termite life cycle optimizer (TLCO), which is based on both the life cycle of a termite colony and the modulation of movement strategies used by many animal species in nature. Termite colonies are comprised of three distinct castes: the workers, the soldiers and the reproductive termites. Each caste undertakes a set of specific tasks that ensure the growth and survival of the colony. TLCO mimics the activities of these three castes that are implemented in a mathematical model. The model is then used to find the global optimum in classic optimization problems. First, the behaviors of the workers, soldiers and reproductive termites are used to simulate a balance between the tasks of exploration and exploitation. Second, the initial population securely records the information obtained at each iteration and transmits it to workers and soldiers at the next iteration. This process is repeated until the global optimum is found with the smallest error. Besides, a new proposed function combined with Lévy flight is used to modulate the movement of termites that increases its flexibility. Thus, TLCO can cover both long distances during the first iterations to improve the convergence rate and shorter distances during the last iterations to enhance the level of accuracy. We then compare the performances of TLCO with other well-known nature-inspired algorithms using 23 classical benchmark functions, CEC2005 benchmark functions, and five real engineering design problems. The results demonstrate the effectiveness and reliability of TLCO in solving these optimization problems. Source codes of TLCO is publicly available at http://goldensolutionrs.com/termite-life-cycle-optimizer.html.

Oppositional Slime Mould Algorithm: Development and application for designing demand side management controller

In recent years, application of several metaheuristic optimization algorithms have been reported. Their flexibility and adaptive structure make them compatible for real engineering design problems. The ability of the optimization algorithm for solving any design problem depends upon the balance between exploration and exploitation phase of the algorithm. These phases are determined by inherent mathematical equations. For making any algorithm compatible for engineering design tasks, certain modifications are required. In this work, we apply two modifications namely (opposition theory and sine cosine based position update mechanism for enhancing exploration and exploitation virtues of recently introduced Slime Mould Algorithm (SMA). Further, the suitability of designing a Demand Side Management Controller by proposed algorithm is explored with the help of standard mathematical analysis and bench marking procedures. Comparison of DSM controllers based on different algorithms has also been evaluated. The results reveal that proposed modifications enhances the optimization capability of SMA.

A new hybrid method based on Aquila optimizer and tangent search algorithm for global optimization.

Since no single algorithm can provide the optimal solutions for all problems, new metaheuristic methods are always being proposed or developed by combining current algorithms or creating adaptable versions. Metaheuristic methods should have a balanced exploitation and exploration stages. One of these two talents may be sufficient in some metaheuristic methods, while the other may be insufficient. By integrating the strengths of the two algorithms and hybridizing them, a more efficient algorithm can be formed. In this paper, the Aquila optimizer-tangent search algorithm (AO-TSA) is proposed as a new hybrid approach that uses the intensification stage of the tangent search algorithm (TSA) instead of the limited exploration stage to improve the Aquila optimizer’s exploitation capabilities (AO). In addition, the local minimum escape stage of TSA is applied in AO-TSA to avoid the local minimum stagnation problem. The performance of AO-TSA is compared with other current metaheuristic algorithms using a total of twenty-one benchmark functions consisting of six unimodal, six multimodal, six fixed-dimension multimodal, and three modern CEC 2019 benchmark functions according to different metrics. Furthermore, two real engineering design problems are also used for performance comparison. Sensitivity analysis and statistical test analysis are also performed. Experimental results show that hybrid AO-TSA gives promising results and seems an effective method for global solution search and optimization problems.

High-Fidelity Surrogate Based Multi-Objective Optimization Algorithm

The employment of conventional optimization procedures that must be repeatedly invoked during the optimization process in real-world engineering applications is hindered despite significant gains in computing power by computationally expensive models. As a result, surrogate models that require far less time and resources to analyze are used in place of these time-consuming analyses. In multi-objective optimization (MOO) problems involving pricey analysis and simulation techniques such as multi-physics modeling and simulation, finite element analysis (FEA), and computational fluid dynamics (CFD), surrogate models are found to be a promising endeavor, particularly for the optimization of complex engineering design problems involving black box functions. In order to reduce the expense of fitness function evaluations and locate the Pareto frontier for MOO problems, the automated multiobjective surrogate based Pareto finder MOO algorithm (AMSP) is proposed. Utilizing data samples taken from the feasible design region, the algorithm creates three surrogate models. The algorithm repeats the process of sampling and updating the Pareto set, by assigning weighting factors to those surrogates in accordance with the values of the root mean squared error, until a Pareto frontier is discovered. AMSP was successfully employed to identify the Pareto set and the Pareto border. Utilizing multi-objective benchmark test functions and engineering design examples such airfoil shape geometry of wind turbine, the unique approach was put to the test. The cost of computing the Pareto optima for test functions and real engineering design problem is reduced, and promising results were obtained.

GGWO: Gaze cues learning-based grey wolf optimizer and its applications for solving engineering problems

In this article, an improved variant of the grey wolf optimizer (GWO) named gaze cues learning-based grey wolf optimizer (GGWO) is proposed. The main intentions are to reduce the existing high selective pressure and low diversification of the GWO algorithm, which results in premature convergence, local optima trapping, and stagnation problems. The GGWO algorithm benefits from two new search strategies: neighbor gaze cues learning (NGCL) and random gaze cues learning (RGCL) inspired by the gaze cueing behavior in wolves. The NGCL strategy enhances the exploitation ability and local optima avoidance. The RGCL, however, boosts the population diversity and balance between exploration and exploitation. The cooperation among three search strategies GWO, NGCL, and RGCL, improves diversification, exploration, and exploitation. The GGWO algorithm performance was evaluated by conducting CEC'18 test functions. Furthermore, the results of GGWO were compared with nine metaheuristic algorithms KH, iwPSO, WOA, GWO, GWO-EPD, HGWOSCA, EEGWO, BOA, and VAGWO. Moreover, the experimental results were statistically analyzed by the Wilcoxon signed-rank and Friedman tests. Additionally, four real engineering design problems and two problems of optimal power flow (OPF) for the IEEE 30-bus and IEEE 118-bus are optimized to verify the applicability of the GGWO in practice. The results show that the GGWO algorithm has been able to provide competitive and superior results to the compared algorithms, and it is capable of solving engineering problems.

Comparative Assessment Of Light-based Intelligent Search And Optimization Algorithms

Classical optimization and search algorithms are not effective for nonlinear, complex, dynamic large-scaled problems with incomplete information. Hence, intelligent optimization algorithms, which are inspired by natural phenomena such as physics, biology, chemistry, mathematics, and so on have been proposed as working solutions over time. Many of the intelligent optimization algorithms are based on physics and biology, and they work by modelling or simulating different nature-based processes. Due to philosophy of constantly researching the best and absence of the most effective algorithm for all kinds of problems, new methods or new versions of existing methods are proposed to see if they can cope with very complex optimization problems. Two recently proposed algorithms, namely ray optimization and optics inspired optimization, seem to be inspired by light, and they are entitled as light-based intelligent optimization algorithms in this paper. These newer intelligent search and optimization algorithms are inspired by the law of refraction and reflection of light. Studies of these algorithms are compiled and the performance analysis of light-based i ntelligent optimization algorithms on unconstrained benchmark functions and constrained real engineering design problems is performed under equal conditions for the first time in this article. The results obtained show that ray optimization is superior, and effectively solves many complex problems.

HPSO-SA: hybrid particle swarm optimization-simulated annealing algorithm for relay node selection in wireless body area networks

In the modern world, wireless body area networks (WBANs) play an essential role in psychological and biomedical applications. The use of WBANs in medical applications is limited due to various issues related to the sensors, viz., irregularity in data production, replacement and recharging of their batteries and the energy consumed by the networks. This manuscript addresses how these problems can be solved along with optimization of the energy consumption through efficient design of the system by applying routing protocols and heuristic-based optimization algorithms. In this paper, the particle swarm optimization (PSO) algorithm is a heuristic search algorithm that relies on an upgrade mechanism of the velocity and position of swarms. Although PSO has excellent exploration capability in global search, it becomes quickly stuck in local minima. To enhance the local search function of the current PSO algorithm, a simulated annealing (SA) algorithm has been incorporated in the exploitation phase. The newly developed hybrid PSO-SA (hPSO-SA) algorithm is validated with other state-of-the-art nature-inspired algorithms on eighteen benchmarks and five real engineering design problems. The statistical results of the proposed hPSO-SA algorithm are promising and indicate very good efficiency. The paper also aims at the application of the proposed algorithm to the WBAN design problem for minimization of the energy consumption through better selection of the relay node. The proposed hPSO-SA algorithm outperforms twelve other metaheuristic algorithms, taking hybrid variants for comparison.

A memory-based Grey Wolf Optimizer for global optimization tasks

Grey Wolf Optimizer (GWO) is a new nature-inspired metaheuristic algorithm based on the leadership and social behaviour of grey wolves in nature. It has shown potential to solve several real-life applications, but still for some complex optimization tasks, it may face the problem of getting trapped at local optima and premature convergence. Therefore, in this study, to prevent from these drawbacks and to get a more stable sense of balance between exploitation and exploration, a new modified GWO called memory-based Grey Wolf Optimizer (mGWO) is proposed. In the mGWO, the search mechanism of the wolves is modified based on the personal best history of each individual wolves, crossover and greedy selection. These strategies help to enhance the global exploration, local exploitation and an appropriate balance between them during the search procedure. To investigate the effectiveness of the proposed mGWO, it has been tested on standard and complex benchmarks given in IEEE CEC 2014 and IEEE CEC 2017. Furthermore, some real engineering design problems and multilevel thresholding problem are also solved using the mGWO. The results analysis and its comparison with other algorithms demonstrate the better search-efficiency, solution accuracy and convergence rate of the proposed mGWO in performing the global optimization tasks.

Balancing composite motion optimization

Meta-heuristic algorithms play an important role in the optimization field thanks to their robustness and programming simplicity. Many meta-heuristic methods have been devised in recent years. Inspired by nature, they usually simulate natural or human-specific phenomena in a better way. A large amount of them are based on complicated behaviors requiring several implementation steps and algorithm-specific control parameters, which impedes users and limits solutions to different types of optimization problems. Hence, designing effective simple and parameter-free optimization methods attracts much attention. In this paper, we propose a novel population-based optimization algorithm based on balancing composite motions (BCMO). The core idea is balancing composite motion properties of individuals in solution space. Equalizing global and local searches via a probabilistic selection model creates a movement mechanism of each individual. Four test suites selected in the literature, which vary from numerical benchmarks to practical problems, to demonstrate the performance of BCMO include: (1) 23 classical benchmark functions, (2) CEC 2005 benchmark functions, (3) CEC 2014 benchmark functions, and (4) 3 real engineering design problems. The statistical results reveal the promising performance and application of BCMO in a variety of optimization and practical problems with constrained and unknown search spaces.

Hybrid Henry Gas Solubility Optimization Algorithm Based on the Harris Hawk Optimization

Henry Gas Solubility Optimization Algorithm (HGSO) is a physical-based algorithm based on Henry's Law and simulates the process that the solubility of gas in liquid changes with temperature. The search strategy of the HGSO is very simple, which results in the algorithm having poor exploitation ability and unable to find a more accurate optimal solution. The Harris Hawk optimization algorithm (HHO) has strong exploitation because of its diverse exploitation strategies. In order to make up for the shortcomings of HGSO algorithm, this article proposes an improved Henry gas solubility optimization algorithm (HHO-HGSO) based on the Harris Hawk optimization. During the iteration, taking the escaping energy function of the HHO algorithm as an indicator and determining a threshold, when the escaping energy function value is greater than the threshold, the algorithm conducts the search strategy of the HGSO algorithm, when less than the threshold, executes four exploitation strategies of Harris Hawk. In order to verify the performance of the proposed algorithm, HHO-HGSO is used to optimize CEC2005 and CEC2017 benchmark test functions and solve 4 real engineering design problems. Marine predators algorithm (MPA), Whale optimization algorithm (WOA), Lightning search algorithm (LSA), Water cycle algorithm (WCA), HGSO, and HHO were used to conduct comparative experiments. The simulation results show that the proposed HHO-HGSO algorithm has strong optimization ability. The core code of the paper has been uploaded in https://github.com/xwaiyy123/HHO-HGSO.

Enhance chaotic gravitational search algorithm (CGSA) by balance adjustment mechanism and sine randomness function for continuous optimization problems

The gravitational search algorithm (GSA) is a population-based meta-heuristic optimization algorithm which finds the optimal solution by the law of gravity and attraction between objects. However, as the number of iterations increases, the increase of the quality of the agents makes GSA fall into the local optimal solution more easily, which greatly reduces the exploration capability of the algorithm. Although the chaotic gravitational search algorithm (CGSA) uses chaotic maps for improving diversity to solve this problem, it still has problems with the balance of exploration and exploitation. This paper proposes the balance adjustment based chaotic gravitational search algorithm (BA-CGSA), which introduces the sine randomness function and the balance mechanism to solve the above problem. 30 benchmark functions of IEEE CEC 2014 are adopted to evaluate the performance of the proposed algorithm in terms of exploration and exploitation. Meanwhile, a real engineering design problem is used to illustrate the ability of the algorithm to solve practical application problems. The experimental results demonstrate its good performance in continuous optimization problems.

A balanced whale optimization algorithm for constrained engineering design problems

• This paper proposes a new advanced whale optimization algorithm for global optimization. • Levy flight and chaotic local search are integrated with whale optimization algorithm. • The effectiveness was proved by the extensive results on benchmark problems and real engineering design problems. In this study, two novel effective strategies composed of Lévy flight and chaotic local search are synchronously introduced into the whale optimization algorithm (WOA) to guide the swarm and further promote the harmony between the inclusive exploratory and neighborhood-informed capacities of the conventional technique and investigate the core searching capabilities of WOA in dealing with optimization tasks. However, the conventional WOA may simply be stuck at local optima or the global best may not be obtained successfully when tackling more complex optimization landscapes, including the multimodal and high dimensional scenarios. To substantiate the efficacy of the enhanced method, it is compared to a set of well-regarded variants of particle swarm optimization and differential evolution. The used benchmark problems are composed of unimodal, multimodal, and fixed-dimensions multimodal functions. Additionally, the proposed balanced method is applied to realize three practical, well-known mathematical models such as tension/compression spring, welded beam, pressure vessel design, three-bar truss design, and I-beam design problems. The experimental results and analysis reveal that the proposed algorithm can outperform other competitors in terms of the convergence speed and the quality of solutions. Promisingly, the proposed method can be treated as an effective and efficient auxiliary tool for more complex optimization models and scenarios.

A novel optimization method for solving constrained and unconstrained problems: modified Golden Sine Algorithm

Recently, the metaheuristic optimization algorithms inspired by nature and different science branches have been powerful solution methods for unconstrained, constrained, and engineering problems. Various metaheuristic optimization algorithms have been proposed and they have been applied to problems in different fields. This paper proposes a novel optimization method based on a modified version of the Golden Sine Algorithm for solving unconstrained, constrained, and engineering problems. The basic idea behind the proposed modified Golden Sine Algorithm (GoldSA-II) depends on finding the optimum solution field in search space by using the decreasing pattern of the sine function and the golden ratio. The performance of the proposed GoldSA-II is evaluated using 19 unconstrained benchmark functions, five constrained optimization test problems, and five real engineering design problems. The results of the proposed GoldSA-II are compared with best-known optimization algorithms using some well-known criteria. The obtained results show that the GoldSA-II converges more accurately to the global solution in many benchmark functions.

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.

Development of an automated aircraft subsystem architecture generation and analysis tool

Purpose – The purpose of this paper is to present a new computational framework to address future preliminary design needs for aircraft subsystems. The ability to investigate multiple candidate technologies forming subsystem architectures is enabled with the provision of automated architecture generation, analysis and optimization. Main focus lies with a demonstration of the frameworks workings, as well as the optimizers performance with a typical form of application problem. Design/methodology/approach – The core aspects involve a functional decomposition, coupled with a synergistic mission performance analysis on the aircraft, architecture and component levels. This may be followed by a complete enumeration of architectures, combined with a user defined technology filtering and concept ranking procedure. In addition, a hybrid heuristic optimizer, based on ant systems optimization and a genetic algorithm, is employed to produce optimal architectures in both component composition and design parameters. The optimizer is tested on a generic architecture design problem combined with modified Griewank and parabolic functions for the continuous space. Findings – Insights from the generalized application problem show consistent rediscovery of the optimal architectures with the optimizer, as compared to a full problem enumeration. In addition multi-objective optimization reveals a Pareto front with differences in component composition as well as continuous parameters. Research limitations/implications – This paper demonstrates the frameworks application on a generalized test problem only. Further publication will consider real engineering design problems. Originality/value – The paper addresses the need for future conceptual design methods of complex systems to consider a mixed concept space of both discrete and continuous nature via automated methods.

An interactive dynamic approach based on hybrid swarm optimization for solving multiobjective programming problem with fuzzy parameters

Real engineering design problems are generally characterized by the presence of many often conflicting and incommensurable objectives. Naturally, these objectives involve many parameters whose possible values may be assigned by the experts. The aim of this paper is to introduce a hybrid approach combining three optimization techniques, dynamic programming (DP), genetic algorithms and particle swarm optimization (PSO). Our approach integrates the merits of both DP and artificial optimization techniques and it has two characteristic features. Firstly, the proposed algorithm converts fuzzy multiobjective optimization problem to a sequence of a crisp nonlinear programming problems. Secondly, the proposed algorithm uses H-SOA for solving nonlinear programming problem. In which, any complex problem under certain structure can be solved and there is no need for the existence of some properties rather than traditional methods that need some features of the problem such as differentiability and continuity. Finally, with different degree of α we get different α-Pareto optimal solution of the problem. A numerical example is given to illustrate the results developed in this paper.

A Distributed Mechanical System Simulation Platform Based on a “Gluing Algorithm”

Three key concepts are presented in this paper, which comprise the foundation of a distributed simulation platform for design and virtual prototyping of general mechanical systems that have their subsystems distributed amongst dispersed development units in multilayered supply chains. First, a general and efficient model description for simulation is defined using XML. Each model is described with an XML file and stored in model database. A complete model can then be assembled based on these model descriptions. Simulation of a model is started simply by sending the model description to a simulation server and running it through a web-based graphics user interface. Second, a new gluing algorithm, denoted as the T-T method, is developed, which enables distributed simulations (both the component models and simulation of the components) to be coupled while maintaining the independence of the separate component simulations. Third, a logical distributed simulation architecture is laid out that can be implemented with one of the existing technologies for distributed computing. Interfaces between different network components have been standardized to enable extensibility of the architecture. These concepts have been incorporated into a prototype web-based distributed simulation system that demonstrates the potential of the new techniques for solving real engineering design problems.