The set covering problem is a formal model for many practical optimization problems. In the set covering problem the goal is to choose a subset of the columns of minimal cost that covers every row. Here, we present a novel application of the artificial bee colony algorithm to solve the non-unicost set covering problem. The artificial bee colony algorithm is a recent swarm metaheuristic techniqu...

This paper deals with the Vehicle Routing Problem with Simultaneous Pickup and Delivery (VRPSPD). A procedure based on the Iterated Local Search (ILS) metaheuristic that uses the Variable Neighborhood Descent (VND) method for performing the local search is proposed. According to literature, the most successful algorithms for the VRPSPD are pure or hybrid versions of the Tabu Search metaheuristi...

A parallel approach to flexible job shop scheduling problem is presented in this paper. We propose two double-level parallel metaheuristic algorithms based on the new method of the neighborhood determination. Algorithms proposed here include two major modules: the machine selection module refer to executed sequentially, and the operation scheduling module executed in parallel. On each level a m...

Most optimization problems in real life applications are often highly nonlinear. Local optimization algorithms do not give the desired performance. So, only global optimization algorithms should be used to obtain optimal solutions. This paper introduces a new nature-inspired metaheuristic optimization algorithm, called Hoopoe Heuristic (HH). In this paper, we will study HH and validate it again...

Nondeterministic metaheuristic optimization and digital image processing are two very different research fields, both extremely active and applicable. They touch in a very limited area, but that narrow interaction opens new very promising applications for digital image processing and new and different deployment of metaheuristic optimization. Multilevel image thresholding is very important for ...

Heat transfer search (HTS) is a novel metaheuristic optimization algorithm that simulates the laws of thermodynamics and heat transfer. In this study, the HTS algorithm is adapted to truss structure optimization. Sizing optimization searches for the minimum weight of a structure subject to stress and displacement constraints. Three truss structures often taken as benchmarks in the optimization ...

Using advanced techniques of econometrics and a metaheuristic optimization approach, this study attempts to evaluate the potential advantages of international portfolio diversification for East Asian international investors when investing in the Middle Eastern emerging markets. Overall, the results of both econometric and the metaheuristic optimization methods are supporting each other. Finding...

This work proposes a metaheuristic-based approach to hyperparameter selection in multilayer perceptron classify EMG signals. The main goal of the study is improve performance model by optimizing four important hyperparameters: number neurons, learning rate, epochs, and training batches. proposed this shows that optimization using particle swarm gray wolf optimizer significantly improves classif...

The Weighted Set Covering Problem is a formal model for many practical optimization problems. In this problem the goal is to choose a subset of columns of minimal cost covering every row. Here, we present a novel application of the Artificial Bee Colony algorithm to solve the Weighted Set Covering Problem. The Artificial Bee Colony algorithm is a recent Swarm Metaheuristic technique based on th...

This paper presents an application of a hierarchical social (HS) metaheuristic to region-based segmentation. The original image is modelled as a simplified image graph, which is successively partitioned into two regions, corresponding to the most significant components of the actual image, until a termination condition is met. The graph-partitioning task is solved as a variant of the min-cut pr...