Spacecraft design optimization is a difficult problem, due to the complexity of optimization cost surfaces and the human expertise in optimization that is necessary in order to achieve good results. In this paper, we propose the use of a set of generic, metaheuristic optimization algorithms (e.g., genetic algorithms, simulated annealing), which is configured for a particular optimization proble...

Harmony Search (HS) is a phenomenon-mimicking algorithm a metaheuristic inspired by the improvisation process of musicians proposed by Zong Woo Geem (2001). Multiobjective, Multiconstratined optimization for determining the Global Optima from several Local Optima is challenging and toughest task, which can be strategically solved by HS metaheuristic algorithm. The capability of the improvisatio...

In this paper, we propose three new metaheuristic implementations to address the problem of minimizing the makespan in a hybrid flexible flowshop with sequence-dependent setup times. The first metaheuristic is a genetic algorithm (GA) embedding two new crossover operators, and the second is an ant colony optimization (ACO) algorithm which incorporates a transition rule featuring lookahead infor...

This paper describes an object-oriented software system for continuous optimization by a new metaheuristic method, the Bat Algorithm, based on the echolocation behavior of bats. Bat algorithm was successfully used for many optimization problems and there is also a corresponding program in MATLAB. We implemented a modified version in C# which is easier for maintenance since it is object-oriented...

We develop a metaheuristic procedure for multiobjective clustering problems. Our goal is to find good approximations of the efficient frontier for this class of problems and provide a means for improving decision making in multiple areas of application and in particular those related to marketing. The procedure is based on the tabu and scatter search methodologies. Clustering problems have been...

As biological inquiry produces ever more network data, such as protein-protein interaction networks, gene regulatory networks, and metabolic networks, many algorithms have been proposed for the purpose of pairwise network alignment– finding a mapping from the nodes of one network to the nodes of another in such a way that the mapped nodes can be considered to correspond with respect to both the...

In recent years, the authors have proposed an effective metaheuristic method for continuous optimization problems based on an analogy of spiral phenomena in nature. This method is called Spiral Optimization (SPO). SPO has two setting parameters: the convergence rate and the rotation rate. Depending on computational and/or problem conditions, the values of these parameters affect search performa...

The indirect communication and foraging behavior of certain species of ants has inspired a number of optimization algorithms for NP-hard problems. These algorithms are nowadays collectively known as the ant colony optimization (ACO) metaheuristic. This chapter gives an overview of the history of ACO, explains in detail its algorithmic components and summarizes its key characteristics. In additi...

Abstract: This paper states a novel, Evolutionary Metaheuristic Based on the Automata Theory (EMODS) for the multiobjective optimization of combinatorial problems. The proposed algorithm uses the natural selection theory in order to explore the feasible solutions space of a combinatorial problem. Due to this, local optimums are often avoided. Also, EMODS exploits the optimization process from t...

The efficiency of any metaheuristic algorithm largely depends on the way of balancing local intensive exploitation and global diverse exploration. Studies show that bat algorithm can provide a good balance between these two key components with superior efficiency. In this paper, we first review some commonly used metaheuristic algorithms, and then compare the performance of bat algorithm with t...