Study Of Bee Colony Optimization Algorithms For Their Applications Into Various Domains

Honey bees are flying insects that are distinguished by their capacity to produce and store honey. Bees live off of nectar and pollen. They fly approximately a few tenths of a mile to gather nectar. Without bees, pollination would be difficult and time consuming. One-third of world's crops are dependant for bee pollination.

A possible solution to the optimization problem is presented by the position of a food source, and the quality (fitness) is measured with the amount of nectar of the associated food source. The number of food sources equals the number of employed bees.

The best thing about being able to make honey bee boxes is they can easily be stacked. This will be the best way to save space if you have a small area dedicated to raising bees. One thing that you will see is the process is similar to assembling wooden drawers.

Honey bee is an important pollinator in the ecosystem and plays an important role in the agriculture. Pollination can increase the value of crops for up to 15 billion dollars ("Colony Collapse Disorder Action Plan."). 1.3 million out of 2.4 million bee colonies are

Optimisation methods are defined by the type of problem that they can begin to solve. The categories of the problems that

Exact optimisation method is the optimisation method that can guarantee to find all optimal solutions. In principle, the optimality of generated solution can be proofed mathematically. Therefore, exact optimisation is also termed as mathematical optimisation. However, exact optimisation approach is impractical usually. The effort of solving an optimisation problem by exact optimisation grows polynomially with the problem size. For example, to solve a problem by brute force approach, the execution time increases exponentially respect to the dimensions of the problem.

Two initial locations for local search (random) and Global search (chaotic) are given and searching is started for initial population. From initial solution (smell), First generation of population size of points (flies) are created. One half of population search locally other half globally for best solution

1 For the broader scientific community. The project will contribute to implementable algorithm and software for the said class of problems which will have applications in real life application in engineering and science. With the scope of

Where Pr is the probability and C(x,y|ω) is the transportation cost. The hybrid intelligence algorithm runs the problem 10,000 times with different environmental criteria. This was done 10 times and the percent error was calculated. These results are shown in Table 3 below.

They compared their annealing scheme leads to results with ω=1 obtained by Angeline [89], and concluded a significant performance improvement on four tested functions. The reducing ω -strategy is a near-optimum setting for many problems, as it allows the swarm to explore the search-space in the beginning of the run, and still manages to shift towards a local search when fine-tuning is required. This was named PSO-TVIW method (PSO with Time-Varying-Inertia-Weight) [90]. At the end, Eberhart and Shi devised an adaptive fuzzy PSO, where a fuzzy controller was employed to control ω over time [91]. This approach is very interesting, as it potentially lets the PSO self-adapt ω to the problem and therefore optimizes and eliminates a parameter of

— The paper reviews the state-of-the-art nature inspired metaheuristic algorithms in optimization, including the Firefly algorithm, PSO algorithms and ABC algorithm. By implementing them in Matlab, we will use worked examples to show how each algorithm works. Firefly algorithm is an evolutionary optimization algorithm, and is inspired by the flashing behavior of special flies called fireflies in nature. There are some noisy non-linear mathematical optimization problems that can be effectively solved by Metaheuristic Algorithms. Firefly algorithm is one of the new metaheuristic algorithms for these optimization problems. The algorithm is inspired by the flashing behavior of fireflies. Firefly Algorithm (FA) is a recent nature inspired optimization algorithm, which simulates the flash pattern and characteristics of fireflies. It is a powerful swarm intelligence algorithm inspired by the flashing phenomenon of the fireflies. The optimization results of both PSO and Firefly are analyzed from the results obtained in Matlab and the results are used to compare both the algorithms.

The combinatorial optimization problems such as Travelling Salesman Problem, Minimum Spanning Tree Problem, Vehicle Routing Problem etc. aims at finding an optimal object from a finite set of objects. Brute force methods which include exhaustive search are not feasible for such problems. In recent years many new and interesting methods are applied for the solution of such problems. These methods such as genetic algorithms (GA), Simulated Annealing, Tabu Search, and Neural Networks are inspired from physical and biological processes.

There are several complexes as well as nonlinear problems which can be solved using a special type of optimization techniques. Some of which are inspired by natural phenomenon like Fish Schooling, Swarm behavior, Ant colony etc. Among these, the PSO is a simple but powerful search technique which gives better results. The PSO algorithm maintains a population of particle where each particle represents a potential solution to an optimization problem, each potential solution in PSO is also associated with a randomized velocity and then the potential solution called particles are flown through the problem space. Let S be the size of swarm where each particle i can be represented as an object with several characteristics. A population of the

The framework of the fuzzy multi-objective particle swarm optimization (FMOPSO) for solving the fuzzy TCQT problem will be presented. The PSO simulates a social behavior scenario such as bird flocking to certain position. A particle is assigned attributes of velocity and position and defined as a point in multi-dimensional space. The particle position depicts a candidate solution to the task at hand. A swarm of particles (birds) fly toward an optimal defined position according to the evolving method that utilizes the best experience (or position) of a particle (called local best) and the best experience (or position) ever identified by any particle (called global best). To show how effective is the proposed fuzzy multi-objective particle swarm optimization

Abstract— This is a new optimization algorithm which mimic the behavior of mosquito to find a hole in mosquito net, if any. Both the flying and sliding motion of the mosquito have been modelled and incorporated in the algorithm. The algorithm was tested for global minima on different type of benchmark functions of various dimension and modality. Namely Gramacy & Lee, Ackley, Rastrigin, Rosenbrock, Griewank and Schwefel functions of one, two, five, ten and thirty dimensions. The experiment was done with thirty different seeds generated randomly for each function and dimension. The algorithm was found to be efficient, convergent and accurate.