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Öğe A symbiotic organisms search algorithm-based design optimization of constrained multi-objective engineering design problems(Emerald Insight, 2021) Üstün, Deniz; Carbas, Serdar; Toktaş, AbdurrahimPurpose In line with computational technological advances, obtaining optimal solutions for engineering problems has become attractive research topics in various disciplines and real engineering systems having multiple objectives. Therefore, it is aimed to ensure that the multiple objectives are simultaneously optimized by considering them among the trade-offs. Furthermore, the practical means of solving those problems are principally concentrated on handling various complicated constraints. The purpose of this paper is to suggest an algorithm based on symbiotic organisms search (SOS), which mimics the symbiotic reciprocal influence scheme adopted by organisms to live on and breed within the ecosystem, for constrained multi-objective engineering design problems. Design/methodology/approach Though the general performance of SOS algorithm was previously well demonstrated for ordinary single objective optimization problems, its efficacy on multi-objective real engineering problems will be decisive about the performance. The SOS algorithm is, hence, implemented to obtain the optimal solutions of challengingly constrained multi-objective engineering design problems using the Pareto optimality concept. Findings Four well-known mixed constrained multi-objective engineering design problems and a real-world complex constrained multilayer dielectric filter design problem are tackled to demonstrate the precision and stability of the multi-objective SOS (MOSOS) algorithm. Also, the comparison of the obtained results with some other well-known metaheuristics illustrates the validity and robustness of the proposed algorithm. Originality/value The algorithmic performance of the MOSOS on the challengingly constrained multi-objective multidisciplinary engineering design problems with constraint-handling approach is successfully demonstrated with respect to the obtained outperforming final optimal designs.Öğe Design Optimization of Multi-objective Structural Engineering Problems Via Artificial Bee Colony Algorithm(Springer Link, 2021) Carbas, Serdar; Üstün, Deniz; Toktaş, AbdurrahimThe construction sector constitutes a significant portion of global gross national expenditures with huge financial budget requirements and provides employment for more than one hundred million people. Besides, considering that people spend more than 80% of their time indoors today, it is necessary to make optimal structure designs. This requirement stems from the inadequacy of existing structures in the face of today's changing conditions. Indeed, realistic design optimization of the structures can be done not only by taking into account a single objective but also considering a number of structural criteria. It means that there is inherent multi-purpose in most structural design optimization problems. Thus, it is very difficult engineering task to solve these kinds of problems, as it is necessary to optimize multiple purposes simultaneously to obtain optimal designs. With the help of the improvisation in optimization techniques used for multi-objective structural engineering design, algorithms are provided to achieve the optimal designs by creating a strong synergy between the structural requirements and constraints mentioned in the design specifications. The recent addition to this trend is so-called Artificial Bee Colony (ABC) algorithm which simulates the nectar searching ability of the bees in nature for nutrition. In this chapter, an optimal design algorithm via ABC is proposed in order to obtain the optimum design of multi-objective structural engineering design problems. The applications in design examples have shown the robustness, effectiveness, and reliability of ABC in attaining the design optimization of multi-objective constrained structural engineering design problems.Öğe Implementation of Flower Pollination Algorithm to the Design Optimization of Planar Antennas(Springer Link, 2021) Toktaş, Abdurrahim; Üstün, Deniz; Carbas, SerdarFlower pollination algorithm (FPA) is an outstanding metaheuristic optimization approach among the recently emerged nature-inspired algorithms. It is built on pollination nature of the flowers, classifying into two categories: biotic and abiotic pollinations. It is observed that the performance of FPA has been well demonstrated through diverse engineering design problems, whereas its efficacy in the design optimization of planar antennas, which are the most important concealed elements in the wireless communication systems, is remained curious in the engineering research topics. In this chapter, FPA is hence applied to the design of planar antennas in order to optimize their shapes and dimensions for the objective function based on resonant bandwidth. The design optimization is carried out through a cooperating platform constituted in this work, communicating MATLAB® with a full-wave simulator named Hyperlynx® 3D EM. Four different planar antennas are hereby designed and optimized for modern wireless communication across a step-by-step procedure. The finally optimized antenna geometries are provided with elaborate dimensions and their performance parameters such as operating frequency band, radiation gain pattern, and peak gain are examined. Therefore, it is shown off that FPA is also effective and successful in the design optimization of planar antennas.Öğe Multi-objective Optimization of Engineering Design Problems Through Pareto-Based Bat Algorithm(Springer Link, 2021) Üstün, Deniz; Carbas, Serdar; Toktaş, AbdurrahimAlthough 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.
