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Multi-objective Optimization of Engineering Design Problems Through Pareto-Based Bat Algorithm

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Applications of Bat Algorithm and its Variants

Part of the book series: Springer Tracts in Nature-Inspired Computing ((STNIC))

Abstract

Although various optimization methods for solving single-objective problems have been developed in the last few decades, these methods have lost their eligibility due to the fact that today’s engineering problems are toward multiple objective optimization problems, in real applications. For single-objective optimization problems, for example, in case of a minimization problem, this value is the decision vector giving the smallest objective that can be achieved within the specified constraints. Hence the minimum decision vector within all possible (feasible) solution vectors is the so-called optimal solution and/or optimal design. However, in multi-objective optimization problems, since a different objective value is generated against each decision vector, the superiority of the solutions over each other is determined by considering the trade-off among the objective values. Therefore, the solution of multi-objective optimization problems, unlike single-objective problems, is a set of vectors rather than a single decision vector. In multi-objective optimization problems, especially if there are intricate objectives, the computational cost of the problem increases. In other words, while synchronously trying to maximize one of the objectives and to minimize another one makes it difficult to find the global optimum design. One of the important techniques used in multi-objective optimization problems is Pareto optimality which enables to select the global optimum solution taking into account the trade-off among all objectives. In this context, using of derivative-based methods has decreased, but the use of metaheuristic methods has increased due to the rapid availability of global optimum solution. This is because the improvements in the field of optimization are progressing in proportion to technology and varying according to the needs. In this chapter, one of the recent metaheuristic optimization methods based on swarm intelligence that is so-called a Pareto-based bat algorithm inspired by the behavior of determining the direction and distance of an object using the echo of the sound called the echolocation of bats is used in order to obtain optimum solutions for multi-objective engineering design problems. In this regard, a four-bar planar truss, a real-sized welded steel beam as well as a multi-layer radar absorber are selected as multi-objective engineering design optimization problems. In case the obtained results (optimal designs) are examined, the potency and the reliability of the proposed multi-objective Pareto-based bat algorithm are demonstrated.

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Author information

Authors and Affiliations

  1. Department of Computer Engineering, Faculty of Engineering, Karamanoglu Mehmetbey University, Karaman, Turkey

    Deniz Ustun

  2. Department of Civil Engineering, Faculty of Engineering, Karamanoglu Mehmetbey University, Karaman, Turkey

    Serdar Carbas

  3. Department of Electrical and Electronics Engineering, Faculty of Engineering, Karamanoglu Mehmetbey University, Karaman, Turkey

    Abdurrahim Toktas

Authors
  1. Deniz Ustun
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  2. Serdar Carbas
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  3. Abdurrahim Toktas
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Corresponding author

Correspondence to Serdar Carbas .

Editor information

Editors and Affiliations

  1. Department of Information Technology, Techno India College of Technology, Kolkata, West Bengal, India

    Nilanjan Dey

  2. Department of Electronics and Instrumentation Engineering, St. Josephs College of Engineering, Chennai, Tamil Nadu, India

    V. Rajinikanth

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Ustun, D., Carbas, S., Toktas, A. (2021). Multi-objective Optimization of Engineering Design Problems Through Pareto-Based Bat Algorithm. In: Dey, N., Rajinikanth, V. (eds) Applications of Bat Algorithm and its Variants. Springer Tracts in Nature-Inspired Computing. Springer, Singapore. https://doi.org/10.1007/978-981-15-5097-3_2

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