Mehmet Karamanoglu

Flower pollination algorithm: A novel approach for multiobjective optimization

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.

Multi-objective Flower Algorithm for Optimization

Flower pollination algorithm is a new nature-inspired algorithm, based on the characteristics of flowering plants. In this paper, we extend this flower algorithm to solve multi-objective optimization problems in engineering. By using the weighted sum method with random weights, we show that the proposed multi-objective flower algorithm can accurately find the Pareto fronts for a set of test functions. We then solve a bi-objective disc brake design problem, which indeed converges quickly.

Using personality type differences to form engineering design teams

Abstract This paper argues for the greater use of personality type instruments such as the Myers-Briggs Type Indicator (MBTI) and the Keirsey Temperament Sorter II (KTS II), when forming engineering design teams. Considering the importance of teamwork in all aspects of education and industry, it is surprising that few universities in the UK use personality type information when forming design teams. This has led to many courses not getting the best out of their students, and more importantly the students not getting the most out of the teamworking experience. Various team formation methods are discussed and their relative strengths and weaknesses outlined. Normal personality type distributions in base populations are presented and compared with data from recent studies of engineering students, and the link between engineering, design and creativity is discussed. The results of this study have shown that the most important of the type preferences is the Sensing-iNtuitive (S-N) scale, with its proven link to creativity and learning styles. It is concluded that both engineers and designers have much in common, and a methodology of using personality type choice sets to select and form engineering design teams is proposed.

A framework for self-tuning optimization algorithm

The performance of any algorithm will largely depend on the setting of its algorithm-dependent parameters. The optimal setting should allow the algorithm to achieve the best performance for solving a range of optimization problems. However, such parameter tuning itself is a tough optimization problem. In this paper, we present a framework for self-tuning algorithms so that an algorithm to be tuned can be used to tune the algorithm itself. Using the firefly algorithm as an example, we show that this framework works well. It is also found that different parameters may have different sensitivities and thus require different degrees of tuning. Parameters with high sensitivities require fine-tuning to achieve optimality.

Cuckoo search for business optimization applications

Cuckoo search has become a popular and powerful metaheuristic algorithm for global optimization. In business optimization and applications, many studies have focused on support vector machine and neural networks. In this paper, we use cuckoo search to carry out optimization tasks and compare the performance of cuckoo search with support vector machine. By testing benchmarks such as project scheduling and bankruptcy predictions, we conclude that cuckoo search can perform better than support vector machine.

Swarm Intelligence and Bio-Inspired Computation

Swarm intelligence (SI) and bio-inspired computing in general have attracted great interest in almost every area of science, engineering, and industry over the last two decades. In this chapter, we provide an overview of some of the most widely used bio-inspired algorithms, especially those based on SI such as cuckoo search, firefly algorithm, and particle swarm optimization. We also analyze the essence of algorithms and their connections to self-organization. Furthermore, we highlight the main challenging issues associated with these metaheuristic algorithms with in-depth discussions. Finally, we provide some key, open problems that need to be addressed in the next decade.

Bat algorithm for topology optimization in microelectronic applications

In many design applications, designers often have to find the best geometrical configurations so as to achieve certain objectives with the minimum amount of materials used. Such shape or topology optimization problems are usually much harder to solve than nonlinear optimization problems in a fixed domain. In this paper, we use the recently developed bat algorithm to solve topology optimization problems. Results show that the distribution of different topological characteristics such as materials can be achieved efficiently. We have also tested the bat algorithm by solving nonlinear design benchmarks. Results suggest that bat algorithm is very efficient for solving nonlinear global optimization problems as well as topology optimization.

Development of a Novel Platform for Greater Situational Awareness in the Urban Military Terrain

The conflicts in Afghanistan and Iraq and the more recent war in the Gaza Strip have emphasized the need for novel platforms which provide for greater situational awareness in the urban terrain. Without intelligent systems, which can accurately provide real-time information, collateral damage to property will result, together with unnecessary civilian deaths. This situation is exacerbated by the fact that within the next decade 75% of the worlds population will be living in urban areas. This paper outlines the current state of unmanned aerial vehicles throughout the world and presents a novel design of a multiple rotary wing platform which has great potential for both military and civilian application areas.

Global Convergence Analysis of the Flower Pollination Algorithm: A Discrete-Time Markov Chain Approach

Flower pollination algorithm is a recent metaheuristic algorithm for solving nonlinear global optimization problems. The algorithm has also been extended to solve multiobjective optimization with promising results. In this work, we analyze this algorithm mathematically and prove its convergence properties by using Markov chain theory. By constructing the appropriate transition probability for a population of flower pollen and using the homogeneity property, it can be shown that the constructed stochastic sequences can converge to the optimal set. Under the two proper conditions for convergence, it is proved that the simplified flower pollination algorithm can indeed satisfy these convergence conditions and thus the global convergence of this algorithm can be guaranteed. Numerical experiments are used to demonstrate that the flower pollination algorithm can converge quickly in practice and can thus achieve global optimality efficiently.

Mathematical modelling and parameter optimization of pulsating heat pipes

Proper heat transfer management is important to key electronic components in microelectronic applications. Pulsating heat pipes (PHP) can be an efficient solution to such heat transfer problems. However, mathematical modelling of a PHP system is still very challenging, due to the complexity and multiphysics nature of the system. In this work, we present a simplified, two-phase heat transfer model, and our analysis shows that it can make good predictions about startup characteristics. Furthermore, by considering parameter estimation as a nonlinear constrained optimization problem, we have used the firefly algorithm to find parameter estimates efficiently. We have also demonstrated that it is possible to obtain good estimates of key parameters using very limited experimental data.