Multi-objective ant lion optimizer: a multi-objective optimization algorithm for solving engineering problems Introduction
1.1 Multi-objective optimization: Optimization techniques are reflected as one of the finest techniques for finding optimal design using machines. Multi-objective optimization “The main focus of this work” deals with finding solutions for problems having more than one objectives. And obviously there is more than one solution for such problems due to the nature of multi-objective problems. While mono-objective problem has only one global optimum. Multiple objectives is the main challenge in multi-objective optimization. The responsibility of a stochastic multi-objective optimization algorithm is to find out the set of
…show more content…
Ai,j = the value of the j-th variable (dimension) of i-th ant n = the number of ants d = the number of variables.
Position of an ant refers the parameters for a particular solution. Matrix MAnt has been reflected to save the position of all ants (variables of all solutions) during optimization. For evaluating each ant, a fitness (objective) function is utilized during optimization and the following matrix stores the fitness value of all ants:
Where, MOA is the matrix for saving the fitness of each ant Ai,j = the value of j-th dimension of i-th ant n = the number of ants f = the objective function.
In addition to ants we assume the antlions are also hiding somewhere in the search space. In order to save their positions and fitness values following matrices are utilized: Where, MAntlion is the matrix for saving the position of each antlion ALi,j = the j-th dimension’s value of i-th antlion n = the number of antlions, and d is the number of variables (dimension). Where, MOAL is the matrix for saving the fitness of each antlion
ALi,j = the j-th dimension’s value of i-th antlion n = number of antlions f = objective function. During optimization following conditions are
Noori, S., Feylizadeh, M. R., Bagherpour, M., Zorriassatine, F., & Parkin, R. M. (2008). Optimization of material requirement planning by fuzzy multi-objective linear programming. Proceedings of the Institution of Mechanical Engineers, 222, 887-900. Retrieved from http://search.proquest.com/docview/195144743?accountid=32521
−→ C = 2 −→ r 2 (14) where components of −→ a are linearly decreased from 2 to 0 over the course of iterations and r 1 , r2 are random vectors in [0, 1]. The hunt is usually guided by the alpha. The beta and delta might also participate in hunting occasionally. In order to mathematically simulate the hunting behavior of grey wolves, the alpha (best candidate solution) beta, and delta are assumed to have better knowledge about the potential location of prey. The first three best solutions obtained so far and oblige the other search agents (including the omegas) to update their positions according to the position of the best search agents.
But the ants that showed up at our experiment were total morons. You'd watch one, and it would sprint up to a Cocoa Krispie, and then stop suddenly, as if saying: "Yikes! Compared with me, this Cocoa Krispie is the size of a Buick!" then it would sprint off in a random direction. Sometimes it would sprint back; sometimes it would sprint to another Cocoa Krispie and act surprised again. but it never seemed to do anything. There were thousands of ants behaving this way, and every single time two of them met, they'd both stop and exchange "high-fives" with their antennas, along with, I assume, some kind of ant pleasantries ("Hi Bob! "No, I'm Bill!" "Sorry! You look just like Bob!"). This was repeated millions of times. I watched these ants for two days, and they accomplished nothing. It was exactly like highway construction. It wouldn't have surprised me if some ants started waving orange flags to direct other insects around the area.
This model can be applied to a wide range of situations based on the factor to be maximized. Honeybees apparently maximize energetic efficiency, but other factors are possible (Schmid-Hempel et al. 1985). For example, the starling (Sturnus vulgaris) apparently maximizes feeding rate according to the marginal value theorem (Kacelnik 1984).
Ants form colonies that range in size from a few dozen predatory individuals living in
Genetic Algorithms provide a holistic search process based on principles of natural genetics and survivals of the fittest……
Operations management is concerned with effectiveness and efficiency within an organization. It represents the planning, scheduling, and controlling of activities that transform inputs such as materials and labor into outputs such as products and services. ("Operations Management," n.d.) As the
Each bench mate had one responsibility, both having the responsibility of being the predator, however one of the bench mate assumed responsibility of whom acted as the beak that could had been a fork, a spoon or a knife, while the other bench mate was given the cup that served as the predator’s stomach. The carpet was the environment, and the environment consisted of a population that was home to 300 organisms. The simulation played that harsh realities of life and everyone had to eat as much as they could in the thirty-seconds that was given. Then the number of prey items were eaten were counted and the allele frequency was counted in the survived population. Each item that was consumed was counted and entered into the data, to see how much was eaten by the predators. The process was repeated until the end of the
Genetic Algorithm is a sequential procedure developed from the science involved in genetic behaviour organisms for optimization purpose. Working Principle of GA includes the simulation of evolution theory in which, the initial set of “population” is selected in random, and then successive "generations" of solutions are reproduced till the optimal convergence. Existence of the fittest individual and natural selection operators is the main agenda of GA process. Philosophically one can say that GAs are based on Darwin’ theory of survival of the fittest. Genetic algorithm is a method for solving optimization problems that is based on natural selection, the process that drives biological evolution. Being analogous to genetics, it is a long complex thread of DNAs and RNAs containing the hereditary data, by which a traits of each individual can be determined, as chromosomes. Each trait in living organisms is being coded with some combination of DNAs like A (Adenine), C (Cytosine), T (Thymine) and G (Guanine).
Using chemical treatment to eradicate the fire ants population is helpful but may harm other insects and are costly. Depending on how the chemicals are designed, specifically for fire ants or just pest control, it may harm other useful insects. Another method is the introduction of a new species, one that is the predator of the fire ant. However if the newer species does not primarily harm fire ants and harm other native species a new pandemic was just
Cormen T. H, Leiserson C. E., Rivest R. L. and Stein C. [1990] (2001). “Introduction to Algorithms”, 2nd edition, MIT Press and McGraw-Hill, ISBN 0-262-03293-7, pp. 27–37. Section 2.3: Designing algorithms..
In every organization, different operational functions exist to ensure the smooth learning of the organization. In order for an individual to have the knowhow on how to operate the functions delegated to them they must have implicit knowledge on the functionalities themselves. Understanding markets, customers and the company goals has always proven to be a core starting point for individuals who ply their trade in the organization. The essence of the skills is evident in globalization, cooperate social responsibility and risk management issues. In operations management, the basic principles of operations should be followed to ensure that the profitability of the organization ensures the operation of the organization is
Ants have four growing stages, the egg, larva, pupa, and the adult. There are over 100,000 known species of ants. Each ant colony has at least one or more queens. The queens job is to lay eggs. How did she start her colony?
Seventh, in some groups of insects, truly social behavior has evolved. Social behavior will allow a large population to survive through difficult periods via cooperation in food gathering, food storage, temperature control, and colony
Fortunately, during under-graduation, I got an opportunity to detect the optimum path of a process through my project “Design and Development of PCB Dual Head Drilling Machine”. Additionally, I became aware of a new subject called Six Sigma which aided me in intertwining new optimization techniques into my project to make the process effective. I optimized the creation...