In this study, a new metaheuristic optimization algorithm, called cuckoo search (CS), is introduced for solving structural optimization tasks. The new CS algorithm in combination with Levy flights is first verified using a benchmark nonlinear constrained optimization problem. For the validation against structural engineering optimization problems, CS is subsequently applied to 13 design problems reported in the specialized literature. The performance of the CS algorithm is further compared with various algorithms representative of the state of the art in the area. The optimal solutions obtained by CS are mostly far better than the best solutions obtained by the existing methods. The unique search features used in CS and the implications for future research are finally discussed in detail.

Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems

In this study, we use a new metaheuristic optimization algorithm, called bat algorithm (BA), to solve constraint optimization tasks. BA is verified using several classical benchmark constraint problems. For further validation, BA is applied to three benchmark constraint engineering problems reported in the specialized literature. The performance of the bat algorithm is compared with various existing algorithms. The optimal solutions obtained by BA are found to be better than the best solutions provided by the existing methods. Finally, the unique search features used in BA are analyzed, and their implications for future research are discussed in detail.

Bat algorithm for constrained optimization tasks

Solving Engineering Optimization Problems with the Simple Constrained Particle Swarm Optimizer

/pdf/solving-engineering-optimization-problems-with-the-simple-3zf6zplcqp.pdf

A concept exploration method for product family design

In this paper, we introduce ACO
 MV
: an ant colony optimization (ACO) algorithm that extends the ACO
 R
 algorithm for continuous optimization to tackle mixed-variable optimization problems. In ACO
 MV
, the decision variables of an optimization problem can be explicitly declared as continuous, ordinal, or categorical, which allows the algorithm to treat them adequately. ACO
 MV
 includes three solution generation mechanisms: a continuous optimization mechanism (ACO
 R
), a continuous relaxation mechanism (ACO
 MV
-o) for ordinal variables, and a categorical optimization mechanism (ACO
 MV
-c) for categorical variables. Together, these mechanisms allow ACO
 MV
 to tackle mixed-variable optimization problems. We also define a novel procedure to generate artificial, mixed-variable benchmark functions, and we use it to automatically tune ACO
 MV
's parameters. The tuned ACO
 MV
 is tested on various real-world continuous and mixed-variable engineering optimization problems. Comparisons with results from the literature demonstrate the effectiveness and robustness of ACO
 MV
 on mixed-variable optimization problems.

/pdf/ant-colony-optimization-for-mixed-variable-optimization-3pxush3ib5.pdf

Ant Colony Optimization for Mixed-Variable Optimization Problems

Most current nonlinear mixed discrete programs can only find locally optimal solutions. This paper proposes an optimization method to find the global solution of a nonlinear mixed discrete program. Based on the fact that: “For a discrete variable xi iff xi ∊{k1, k1, k2…,km } then (xi −k 1) (xi k 2)(xi km =0”, the original mixed discrete program is transformed into a penalty optimization program with continuous variables. This penalty optimization program is then solved to find a local optimum. Utilizing the Multi-Level Single Linkage technique, enough starting points are systematically generated to search for most local optima within the feasible region. A global optimum is then found at a pre-specified sufficiently high confidence level such as 99.5%. Some examples of design optimization in literature are tested, which demonstrate that the proposed method is superior to current methods for finding the global optimum.

Chih Tan Chou

Papers

A global approach for nonlinear mixed discrete programming in design optimization