Elsevier

ISA Transactions

Volume 53, Issue 4, July 2014, Pages 1168-1183
ISA Transactions

Interior search algorithm (ISA): A novel approach for global optimization

https://doi.org/10.1016/j.isatra.2014.03.018Get rights and content

Highlights

  • A new idea for global optimization is proposed inspired from interior design and decoration.

  • Two different search mechanisms, composition optimization and mirror search, are introduced.

  • The algorithm has only one parameter to tune which is tuned using three different strategies.

  • The algorithm is used to solve several unconstrained and constrained benchmark problems.

  • The obtained results compared with the results of other algorithms presented in the literature.

Abstract

This paper presents the interior search algorithm (ISA) as a novel method for solving optimization tasks. The proposed ISA is inspired by interior design and decoration. The algorithm is different from other metaheuristic algorithms and provides new insight for global optimization. The proposed method is verified using some benchmark mathematical and engineering problems commonly used in the area of optimization. ISA results are further compared with well-known optimization algorithms. The results show that the ISA is efficiently capable of solving optimization problems. The proposed algorithm can outperform the other well-known algorithms. Further, the proposed algorithm is very simple and it only has one parameter to tune.

Introduction

Metaheuristic optimization algorithms are used extensively for solving complex optimization problems. Compared to conventional methods based on formal logics or mathematical programming, these metaheuristic algorithms are generally more powerful [57]. Diversification and intensification are the main features of the metaheuristic algorithms [67]. The diversification phase guarantees that the algorithm explores the search space more efficiently. The intensification phase searches through the current best solutions and selects the best candidates. Modern metaheuristic algorithms are developed to solve problems faster, to solve large problems, and to obtain more robust methods [60]. The metaheuristic algorithms do not have limitations in using sources (e.g. physic-inspired charged system search [34]).

In this paper, a new metaheuristic algorithm, called interior search algorithm (ISA), is introduced for global optimization. There is another optimization algorithm in the literature called the interior point method. This method is a mathematical programming not a metaheuristic algorithm and, therefore, it is not related to the current algorithm. The ISA takes into account the aesthetic techniques commonly used for interior design and decoration to investigate global optimization problems, therefore, it can also be called aesthetic search algorithm. The performance and efficiency of the ISA are verified using some widely used benchmark problems. The results confirm the applicability of ISA for solving optimization tasks. The ISA can also outperform the existing metaheuristic algorithms. The paper is organized as follows: Section 2 provides a brief review of the metaheuristic algorithms. Section 3 presents the interior design and decoration metaphor and the characteristics of the proposed ISA, including the formulation of the algorithm. Numerical examples are presented in Section 4 to verify the efficiency of the ISA. In Section 5, the performance of the proposed algorithm is also tested using some well-known engineering design problems which have been previously employed to validate different algorithms. Finally, some concluding remarks and suggestions for future research are provided in Section 6.

Section snippets

Metaheuristic algorithms

Optimization techniques can be divided in two groups, mathematical programming and metaheuristic algorithms. In general, the existing metaheuristic algorithms may be divided into two main categories as follows:

  • Evolutionary algorithms

  • Swarm algorithms

Composition design

The interior design procedure follows a coordinated and systematic methodology. This includes research, analysis, and integration of knowledge into the creative process. In order to produce an interior space that fulfills the project goals, the needs and resources of the client should be satisfied [49]. The interior design usually starts from bounds to center. That is to say, a designer commonly starts designing the composition of the elements from the wall and then the space will be limited to

Implementation and numerical experiments

The computational procedures described above have been implemented in a MATLAB™ computer program. In order to evaluate the ISA performance, it is validated using some classical benchmark problems. The benchmarks include both low and high dimension problems, and each class has seven well-known functions. The descriptions of the functions are presented in Table 1. Sphere function is the one of the simplest benchmarks and it is the sum of squares of variables’ components. More details about other

Engineering design problems

Real-world engineering optimization problems are usually non-linear and involve with complex geometrical and mechanical constraints. To assess the performance of the ISA, it was tested using some constrained engineering design problems. The proposed algorithm was first applied to some well-known benchmark engineering problems including welded beam design, pressure vessel design and spring design. Then it applied to a real-world problem, design optimization of 72 bar space truss under multi

Summary and conclusion

In the current study, the ISA is presented as a new paradigm for global optimization. The ISA is inspired by interior design and decoration. The derivative information is not necessary in the ISA since it uses a stochastic random search instead of a gradient search. The proposed algorithm is very simple and it has only one parameter, α, to tune which makes it adaptive to a wider class of optimization problems.

The validity of the ISA method is verified using several benchmark unconstrained and

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