Scheduling tasks on computational grids is known as NPcomplete problem. Scheduling tasks in Grid computing, means assigning tasks to resources such that the time termination and average waiting time criteria and the number of required machines are optimized. Based on heuristic or meta-heuristic search have been proposed to obtain optimal solutions. The presented method tries to optimize all of ...

Objectives: To propose a novel hybrid control plan to enhance the power quality (PQ) of distributed energy sources (DERs) such as solar (SE), wind (WE), and battery storage system (BEES) technologies. Method: A BEES, PV farm, farm are integrated into grid supply home loads. The ABMO-ANN is method that combines Hybrid Barnacles Mating Optimization Algorithm (HBMO) Artificial Neural Network (ANN)...

Marriage in Honey Bees Optimization (MBO) is a new swarm intelligence technique inspired by the marriage process of honey bees. It has been shown to be very effective in solving the propositional satisfiability problem known as 3–SAT (each clause has exactly three literals). The objective of this paper is to test a conventional annealing approach as a basis for determining the pool of drones (f...

The task to generate product design concepts to maintain a particular brand identity whilst meeting functional requirements is challenging to designers. Shape grammars have been shown to be able formally to describe the creation of branded product shapes using a set of shape rules. These shape rules are applied manually to generate a family of new design concepts that maintain the brand identit...

This paper introduces the Particle Swarm Optimization (PSO) algorithm to solve the optimal network reconfiguration problem for power loss reduction. The PSO is a relatively new and powerful intelligence evolution method for solving optimization problems. It is a population-based approach. The PSO was inspired from natural behavior of the bees on how they find the location of most flowers. The p...

This paper presents OptBees, a new bee-inspired algorithm for solving continuous optimization problems. Two key mechanisms for OptBees are introduced: 1) a local search step; and 2) a process of dynamic variation of the number of active bees that helps the algorithm to regulate the computational effort spent in the search and to achieve improved results. The performance of the algorithm was eva...

The artificial bee colony (ABC) algorithm is a swarm-based metaheuristic optimization technique, developed by inspiring foraging and dance behaviors of honey bee colonies. ABC consists of four phases named as initialization, employed bee, onlooker bee and scout bee. The employed bees try to improve their solution in employed bees phase. If an employed bee cannot improve self-solution in a certa...

The Bees Algorithm is a search procedure inspired by the way honey-bees forage for food. A standard mechanical design problem, the design of a welded beam structure, was used to benchmark the Bees Algorithm against other optimisation techniques. The paper presents the results obtained showing the robust performance of the Bees Algorithm.

In this paper, a new discrete artificial bee colony algorithm is used to solve the symmetric traveling salesman problem (TSP). The concept of Swap Operator has been introduced to the original artificial bee colony (ABC) algorithm which can help the bees to generate a better candidate tour by greedy selection. By taken six typical TSP instances as examples, the proposed algorithm is compared wit...

Travelling Salesman Problem (TSP) belongs to the class of NP-Complete problems. It has been proved that evolutionary algorithms are effective and efficient, with respect to the traditional methods for solving NP-Complete problems like TSP, with avoidance trapping in local minima areas. Artificial Bee Colony (ABC) is a new swarm-based optimization algorithm, which inspired by the foraging behavi...