Enhanced Multi-Strategy Particle Swarm Optimization for Constrained Problems with an Evolutionary-Strategies-Based Unfeasible Local Search Operator

Nowadays, optimization problems are solved through meta-heuristic algorithms based on stochastic search approaches borrowed from mimicking natural phenomena. Notwithstanding their successful capability to handle complex problems, the No-Free Lunch Theorem by Wolpert and Macready (1997) states that there is no ideal algorithm to deal with any kind of problem. This issue arises because of the nature of these algorithms that are not properly mathematics-based, and the convergence is not ensured. In the present study, a variant of the well-known swarm-based algorithm, the Particle Swarm Optimization (PSO), is developed to solve constrained problems with a different approach to the classical penalty function technique. State-of-art improvements and suggestions are also adopted in the current implementation (inertia weight, neighbourhood). Furthermore, a new local search operator has been implemented to help localize the feasible region in challenging optimization problems. This operator is based on hybridization with another milestone meta-heuristic algorithm, the Evolutionary Strategy (ES). The self-adaptive variant has been adopted because of its advantage of not requiring any other arbitrary parameter to be tuned. This approach automatically determines the parameters’ values that govern the Evolutionary Strategy simultaneously during the optimization process. This enhanced multi-strategy PSO is eventually tested on some benchmark constrained numerical problems from the literature. The obtained results are compared in terms of the optimal solutions with two other PSO implementations, which rely on a classic penalty function approach as a constraint-handling method

Enhanced Artificial Coronary Circulation System Algorithm for Truss Optimization with Multiple Natural Frequency Constraints

In this paper, an enhanced artificial coronary circulation system (EACCS) algorithm is applied to structural optimization with continuous design variables and frequency constraints. The standard algorithm, artificial coronary circulation system (ACCS), is inspired biologically as a non-gradient algorithm and mimics the growth of coronary tree of heart circulation system. Designs generated by the EACCS algorithm are compared with other popular evolutionary optimization methods, the objective function being the total weight of the structures.Truss optimization with frequency constraints has attracted substantial attention to improve the dynamic performance of structures. This kind of problems is believed to represent nonlinear and non-convex search spaces with several local optima. These problems are also suitable for examining the capabilities of the new algorithms. Here, ACCS is enhanced (EACCS) and employed for size and shape optimization of truss structures and six truss design problems are utilized for evaluating and validating of the EACCS. This algorithm uses a fitness-based weighted mean in the bifurcation phase and runner phase of the optimization process. The numerical results demonstrate successful performance, efficiency and robustness of the new method and its competitive performance to some other well-known meta-heuristics in structural optimization

Seeking multiple solutions:an updated survey on niching methods and their applications

Multi-Modal Optimization (MMO) aiming to locate multiple optimal (or near-optimal) solutions in a single simulation run has practical relevance to problem solving across many fields. Population-based meta-heuristics have been shown particularly effective in solving MMO problems, if equipped with specificallydesigned diversity-preserving mechanisms, commonly known as niching methods. This paper provides an updated survey on niching methods. The paper first revisits the fundamental concepts about niching and its most representative schemes, then reviews the most recent development of niching methods, including novel and hybrid methods, performance measures, and benchmarks for their assessment. Furthermore, the paper surveys previous attempts at leveraging the capabilities of niching to facilitate various optimization tasks (e.g., multi-objective and dynamic optimization) and machine learning tasks (e.g., clustering, feature selection, and learning ensembles). A list of successful applications of niching methods to real-world problems is presented to demonstrate the capabilities of niching methods in providing solutions that are difficult for other optimization methods to offer. The significant practical value of niching methods is clearly exemplified through these applications. Finally, the paper poses challenges and research questions on niching that are yet to be appropriately addressed. Providing answers to these questions is crucial before we can bring more fruitful benefits of niching to real-world problem solving

COMPARISON OF APPROACHES TO 10 BAR TRUSS STRUCTURAL OPTIMIZATION WITH INCLUDED BUCKLING CONSTRAINTS

The complex problem of truss structural optimization, based on the discrete design variables assumption, can be approached through optimizing aspects of sizing, shape, and topology or their combinations. This paper aims to show the differences in results depending on which aspect, or combination of aspects of a standard 10 bar truss problem is optimized. In addition to standard constraints for stress, cross section area, and displacement, this paper includes the dynamic constraint for buckling of compressed truss elements. The addition of buckling testing ensures that the optimal solutions are practically applicable. An original optimization approach using genetic algorithm is verified through comparison with literature, and used for all the optimization combinations in this research. The resulting optimized model masses for sizing, shape, and topology or their combinations are compared. A discussion is given to explain the results and to suggest which combination would be best in a generalized example

A comprehensive survey on cultural algorithms

Peer reviewedPostprin

Eliminating stick-slip vibrations in drill-strings with a dual-loop control strategy optimized by the CRO-SL algorithm

Funding: This work was partially supported by the Spanish Ministerial Commission of Science and Technology (MICYT) through project number TIN2017-85887-C2-2-P Acknowledgments: The authors would like to thank Marian Wiercigroch and Vahid Vaziri from the Centre for Applied Dynamics Research, University of Aberdeen, for providing the realistic drill-string parameters used in this work.Peer reviewedPublisher PD

Structural optimization in steel structures, algorithms and applications

L'abstract è presente nell'allegato / the abstract is in the attachmen

Means and Effects оf Constraining the Number of Used Cross-Sections in Truss Sizing Optimization

This paper looks at sizing optimization results, and attempts to show the practical implications of using a novel constraint. Most truss structural optimization problems, which consider sizing in order to minimize weight, do not consider the number of different cross-sections that the optimal solution can have. It was observed that all, or almost all, cross-sections were different when conducting the sizing optimization. In practice, truss structures have a small, manageable number of different cross-sections. The constraint of the number of different cross-sections, proposed here, drastically increases the complexity of solving the problem. In this paper, the number of different cross-sections is limited, and optimization is done for four different sizing optimization problems. This is done for every number of different cross-section profiles which is smaller than the number of cross-sections in the optimal solution, and for a few numbers greater than that number. All examples are optimized using dynamic constraints for Euler buckling and discrete sets of cross-section variables. Results are compared to the optimal solution without a constrained number of different cross-sections and to an optimal model with just a single cross-section for all elements. The results show a small difference between optimal solutions and the optimal solutions with a limited number of different profiles which are more readily applicable in practice

Ensemble of four metaheuristic using a weighted sum technique for aircraft wing design

Recently, metaheuristics (MHs) have become increasingly popular in real-world engineering applications such as in the design of airframes structures and aeroelastic designs owing to its simple, flexible, and efficient nature. In this study, a novel hybrid algorithm is termed as Ensemble of Genetic algorithm, Grey wolf optimizer, Water cycle algorithm and Population base increment learningusing Weighted sum (E-GGWP-W), based on the successive archive methodology of the weighted population has been proposed to solve the aircraft composite wing design problem. Four distinguished algorithms viz. a Genetic algorithm (GA), a Grey wolf optimizer (GWO), a Water cycle algorithm (WCA), and Population base increment learning (PBIL) were used as ingredients of the proposed algorithm. The considered wing design problem is posed for overall weight minimization subject to several aeroelastic and structural constraints along with multiple discrete design variables to ascertain its viability for real-world applications. The algorithms are validated through the standard test functions of the CEC-RW-2020 test suite and composite Goland wing aeroelastic optimization. To check the performance, the proposed algorithm is contrasted with eight well established and newly developed MHs. Finally, a statistical analysis is done by performing Friedman’s rank test and allocating respective ranks to the algorithms. Based on the outcome, ithas been observed that the suggested algorithm outperforms the others