This paper introduces a novel population-based bio-inspired meta-heuristic optimization algorithm, called Blood Coagulation Algorithm (BCA). BCA derives inspiration from the process of blood coagulation in human body. The underlying concepts and ideas behind proposed algorithm are cooperative behavior thrombocytes their intelligent strategy clot formation. These behaviors modeled utilized to un...

This paper presents a comprehensive survey of the meta-heuristic optimization algorithms on text clustering applications and highlights its main procedures. These Artificial Intelligence (AI) are recognized as promising swarm intelligence methods due to their successful ability solve machine learning problems, especially problems. reviews all relevant literature meta-heuristic-based application...

This study extends a mathematical model that integrates the location, allocation, inventory replenishment and routing decisions simultaneously. To cope with inherent uncertainty of parameters, we implement a continuous-time Markov process and derive the performance measures of the system. Using the obtained results, the problem is formulated as a mixed integer nonlinear programing model (MINLP)...

In this paper, a meta-heuristic method, the Bat Algorithm, based on the echolocation behavior of bats is used to determine the optimum service rate of a queue problem. A finite buffer M/M/1 queue with N policy, multiple working vacations and Bernoulli schedule vacation interruption is considered. Under the two customers' impatient situations, balking and reneging, the...

Flow shop scheduling of jobs has always been a popular problem that has found solutions in the number of heuristic and meta-heuristic techniques. In this manuscript, two-machine flow shop scheduling problem has been investigated while optimizing makespan and idle time of machines. Uncertainties in the processing time and set up times of jobs involved are also taken into consideration in the for...

We describe a hybrid meta-heuristic algorithm for combinatorial optimization problems with a specific reference to the travelling salesman problem (TSP). The method is a combination of a genetic algorithm (GA) and greedy randomized adaptive search procedure (GRASP). A new adaptive fuzzy a greedy search operator is developed for this hybrid method. Computational experiments using a wide range of...

the capacitated clustering problem (ccp) is one of the most importantcombinational optimization problems that nowadays has many real applications inindustrial and service problems. in the ccp, a given n nodes with known demandsmust be partitioned into k distinct clusters in which each cluster is detailed by anode acting as a cluster center of this cluster. the objective is to minimize the sumof...

Nature Inspired Meta-Heuristic algorithms are proved to be beneficial for solving real world combinatorial problems such as minimum spanning tree, knapsack problem, process planning problems, load balancing and many more. In this research work, existing meta-heuristic approaches are discussed. Due to astonishing feature of echolocation, bat algorithm has drawn major attention in recent years an...

production planning and scheduling are the most important issues of the production industries, which have a considerable influence on the productivity of the production systems. although, production planning and scheduling are in two different medium-term and short-term decision making levels, there are a very close relationship between them. ignoring this important feature in production planni...