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JournalISSN: 0305-215X

Engineering Optimization 

Taylor & Francis
About: Engineering Optimization is an academic journal published by Taylor & Francis. The journal publishes majorly in the area(s): Optimization problem & Multi-objective optimization. It has an ISSN identifier of 0305-215X. Over the lifetime, 2568 publications have been published receiving 51547 citations.


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Journal ArticleDOI
TL;DR: Experimental results in terms of the likelihood of convergence to a global optimal solution and the solution speed suggest that the SFLA can be an effective tool for solving combinatorial optimization problems.
Abstract: A memetic meta-heuristic called the shuffled frog-leaping algorithm (SFLA) has been developed for solving combinatorial optimization problems. The SFLA is a population-based cooperative search metaphor inspired by natural memetics. The algorithm contains elements of local search and global information exchange. The SFLA consists of a set of interacting virtual population of frogs partitioned into different memeplexes. The virtual frogs act as hosts or carriers of memes where a meme is a unit of cultural evolution. The algorithm performs simultaneously an independent local search in each memeplex. The local search is completed using a particle swarm optimization-like method adapted for discrete problems but emphasizing a local search. To ensure global exploration, the virtual frogs are periodically shuffled and reorganized into new memplexes in a technique similar to that used in the shuffled complex evolution algorithm. In addition, to provide the opportunity for random generation of improved information,...

1,007 citations

Journal ArticleDOI
TL;DR: A comparison of the proposed algorithm with other algorithms has been made, which shows that the FPA is efficient with a good convergence rate, and the importance for further parametric studies and theoretical analysis is highlighted and discussed.
Abstract: Multiobjective design optimization problems require multiobjective optimization techniques to solve, and it is often very challenging to obtain high-quality Pareto fronts accurately. In this article, the recently developed flower pollination algorithm (FPA) is extended to solve multiobjective optimization problems. The proposed method is used to solve a set of multiobjective test functions and two bi-objective design benchmarks, and a comparison of the proposed algorithm with other algorithms has been made, which shows that the FPA is efficient with a good convergence rate. Finally, the importance for further parametric studies and theoretical analysis is highlighted and discussed.

454 citations

Journal ArticleDOI
TL;DR: This paper focuses on a particular algorithm, Efficient Global Optimization (EGO), that uses kriging metamodels and several infill sampling criteria are reviewed, namely criteria for selecting design points at which the true functions are evaluated.
Abstract: The use of surrogate models or metamodeling has lead to new areas of research in simulation-based design optimization Metamodeling approaches have advantages over traditional techniques when dealing with the noisy responses and/or high computational cost characteristic of many computer simulations This paper focuses on a particular algorithm, Efficient Global Optimization (EGO) that uses kriging metamodels Several infill sampling criteria are reviewed, namely criteria for selecting design points at which the true functions are evaluated The infill sampling criterion has a strong influence on how efficiently and accurately EGO locates the optimum Variance-reducing criteria substantially reduce the RMS error of the resulting metamodels, while other criteria influence how locally or globally EGO searches Criteria that place more emphasis on global searching require more iterations to locate optima and do so less accurately than criteria emphasizing local search

410 citations

Journal ArticleDOI
TL;DR: An improved particle swarm optimizer (PSO) for solving mechanical design optimization problems involving problem-specific constraints and mixed variables such as integer, discrete and continuous variables is presented.
Abstract: This paper presents an improved particle swarm optimizer (PSO) for solving mechanical design optimization problems involving problem-specific constraints and mixed variables such as integer, discrete and continuous variables. A constraint handling method called the ‘fly-back mechanism’ is introduced to maintain a feasible population. The standard PSO algorithm is also extended to handle mixed variables using a simple scheme. Five benchmark problems commonly used in the literature of engineering optimization and nonlinear programming are successfully solved by the proposed algorithm. The proposed algorithm is easy to implement, and the results and the convergence performance of the proposed algorithm are better than other techniques.

382 citations

Journal ArticleDOI
TL;DR: A discrete search strategy using the harmony search (HS) heuristic algorithm is presented in detail and its effectiveness and robustness, as compared to current discrete optimization methods, are demonstrated through several standard truss examples.
Abstract: Many methods have been developed and are in use for structural size optimization problems, in which the cross-sectional areas or sizing variables are usually assumed to be continuous. In most practical structural engineering design problems, however, the design variables are discrete. This paper proposes an efficient optimization method for structures with discrete-sized variables based on the harmony search (HS) heuristic algorithm. The recently developed HS algorithm was conceptualized using the musical process of searching for a perfect state of harmony. It uses a stochastic random search instead of a gradient search so that derivative information is unnecessary. In this article, a discrete search strategy using the HS algorithm is presented in detail and its effectiveness and robustness, as compared to current discrete optimization methods, are demonstrated through several standard truss examples. The numerical results reveal that the proposed method is a powerful search and design optimization tool f...

362 citations

Performance
Metrics
No. of papers from the Journal in previous years
YearPapers
202334
2022132
2021240
2020123
2019120
201862