Many problems that we face nowadays can be expressed as optimization problems. Finding the best solution for real-world instances of such problems is hard or even infeasible. Metaheuristic algorithms have been used for decades to guide the search for satisfactory solutions in hard optimization problems at an affordable cost. However, despite its many benefits, the application of metaheuristics ...

Ant Colony Optimization is one of the metaheuristic algorithms and first member of ACO is Ant System (AS). AS uses a population of co-operating ants also known as agents. The cooperation phenomenon among the ants is called foraging and recruiting behavior. This describes how ants explore the world in search of food sources, then find their way back to the nest and indicate the food source to th...

Taking into account increasing volumes of the international seaborne trade, liner shipping companies have to ensure efficiency of their operations in order to remain competitive. The bunker consumption cost constitutes a substantial portion of the total vessel operating cost and directly affects revenues of liner shipping companies. “Slow steaming” became a common strategy among ocean carriers ...

Metaheuristic algorithm is one of the most popular methods in solving many optimization problems. This paper presents a new hybrid approach comprising of two natures inspired metaheuristic algorithms i.e. Cuckoo Search (CS) and Accelerated Particle Swarm Optimization (APSO) for training Artificial Neural Networks (ANN). In order to increase the probability of the egg’s survival, the cuckoo bird...

Metaheuristic algorithms are becoming an important part of modern optimization. A wide range of metaheuristic algorithms have emerged over the last two decades, and many metaheuristics such as particle swarm optimization are becoming increasingly popular. Despite their popularity, mathematical analysis of these algorithms lacks behind. Convergence analysis still remains unsolved for the majorit...

Most real-world search and optimization problems involve complexities such as non-convexity, nonlinearities, discontinuities, mixed nature of variables, multiple disciplines and large dimensionality, a combination of which renders classical provable algorithms to be either ineffective, impractical or inapplicable. There do not exist any known mathematically motivated algorithms for finding the ...

Engineering optimization needs easy-to-use and efficient optimization tools that can be employed for practical purposes. In this context, stochastic search techniques have good reputation and wide acceptability as being powerful tools for solving complex engineering optimization problems. However, increased complexity of some metaheuristic algorithms sometimes makes it difficult for engineers t...

Computational cost of metaheuristic based optimum design algorithms grows excessively with structure size. This results in computational inefficiency of modern metaheuristic algorithms in tackling optimum design problems of large scale structural systems. This paper attempts to provide a computationally efficient optimization tool for optimum design of large scale steel frame structures to AISC...

Abs t r ac t -We address the problem of board-level multiterminal net assignment in FPGA-based logic emulation. We present a novel mathematical model for this problem. A powerful metaheuristic approach, called scatter search, is adopted. Effective heuristics are incorporated for accelerating the optimization search process. Experimental results demonstrate the promising performance of our appro...

This paper proposes a modified sine cosine algorithm (MSCA) for discrete sizing optimization of truss structures. The original sine cosine algorithm (SCA) is a population-based metaheuristic that fluctuates the search agents about the best solution based on sine and cosine functions. The efficiency of the original SCA in solving standard optimization problems of well-known mathematical function...