Simulated Annealing Search Algorithm

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With the aid of the Simulated Annealing algorithm, humans can arrive at a global optimum of a multidimensional function despite the many hills and valleys in the earth (SA). Unlike manual methods of wandering about and climbing mountains and high hills, SA is dynamic in that it never gets stuck. The algorithm looks for the largest nearby point and the highest point in space, but it almost never looks for the lower position. SA makes advantage of probability and calculates e using (-E\/KT). Temperature is denoted by T, while fitness values between the current and potential solutions are denoted by E. The probability's goal is that of reaching the highest point to escape a local optimum or barrier like harsh weather conditions and climate (Manlove, David & Mehlhorn 46).

The Parameter of Temperature (T)

The parameter of temperature (T) is highly kept during the beginning to enhance acceptance probability with slightly poorer solutions but on the current solution higher in the start. This parameter emphasizes exploration on space (Manlove, David & Mehlhorn 52).The neighborhood positions searched as temperature setting is reduced to make the algorithm be able to converge to a solution which is close to global maximum. Simulated Annealing is a technology that has merits in the field of technology. It has been applied widely in areas and has lessened the work done especially in locomotion and distance coverage. The method has achieved these by busting out local optima and search space broadening.

Application of Simulated Annealing

The technique in approximating the global optimum function, SA algorithm as applied in traveling salesman problem. Traveling salesman routine (TST) has an answer to the question of distance coverage. ‘Given two points and the distance between the two places, what route is the shortest as much as possible? This is if one visits each town only once and returns to the origin city?’The method is a special case of vehicle routing problem Applegate et al., Cook 39).

The distance between the towns ABCD has a cost to incur as one person has to travel by air, road or water transport. A salesman, therefore, tries as much as possible to cut the costs and the time spent in covering the whole area. The traveling salesman concept has contributed to the application of SA in science and engineering, especially in circuit board manufacture. These have helped in the determination of the best order a laser can drill many holes. SA has reduced the production costs, in particular for manufacturers.

Simulated Annealing in Mobile Wireless Base Stations

Simulated annealing algorithm is useful in the deployment of mobile wireless base stations (Newman & Michael 46).Wireless connections can be insufficient in a city during a special festival. To improve the network density, Mobile Base Transceiver Stations (MBTS) can be increased by the wireless service providers. Installation of wireless transceivers is costly hence the provider of the wireless provider offers elementary coverage with a regular flow of data communication. Due to its high nature, SA concept application is helpful by increasing the number of mobile base trucks to increase network coverage. MBTS has therefore been found useful and widely used by meteorologists in weather prediction, management of disasters, tracking in traffics and communication in military battles.

Fingerprint Matching and Simulated Annealing

Simulated annealing is helpful in fingerprint matching as a means of identifying people (Newman & Michael 122).Fingerprint detection is used globally as an identification system. Biometrics verification of people’s identities is mainly based on fingerprints. There is a list of matching methods like structure matching, minutiae matching and fingerprint images matching. Popular fingerprint recognition uses algorithm minutiae using as a feature that is simpler to apply and needs a minute capacity to be stored.

Simulated Annealing for Plagiarism Detection

Simulated annealing is helpful for the detection of plagiarism. Given the manuscript, the algorithm goes through the document for instance, the source material, punctuation and case details ignoring. This is simply because the sought strings, single-string searching is in plenty as algorithm is impractical. In many practical cases this algorithms work but can show long running time on particular examples. In this case, 10000 pattern string is searched whereby A's are followed by B's when seeking 10 million A’s. This exhibits the worst-case 0(min) time.

Stable Marriage Problem and Simulated Annealing

Simulated annealing knowledge helps in stable marriage problem (SMP).SMP is also known as Stable Matching Problem, and it is a problem that arises when finding a stable matching in two sets of elements that are equally sized given each item has an ordering of preferences (Sally & Lin 24). Matching is used to refer to the mapping of an object against another, especially for comparison purposes. It is done purposely to cite the similarities and differences between the two elements which might be containing contradicting features. A matching is stable if two elements do not have any match. If there is a match, then it automatically means the two factors are not stable. Simulated Algorithm application is essential in the solving of stable marriage problem. One can be given men and women in sets. If he marries them off in pairs respectively, each man has a rank of the women depending on their preferences. Every woman has too her choices. These marriages are only stable if their forms contain no pairs (Sally & Lin 30).


Simulated Annealing Search Algorithm has therefore had several positive impacts globally. It has led to significant discoveries and in the world of technology. It has helped people solve problems relating to the identification, solved marriage problems enhancing stability in marriage and has assisted in detecting and curbing plagiarism in published works. SA is, therefore, an excellent problem-solving computer technology.

Work cited

Applegate, David L, Robert E. Bixby, Vasek Chvatal, and William J. Cook. The Traveling Salesman Problem: A Computational Study. Princeton: Princeton University Press, 2011. Internet resource.

Manlove, David F, and Kurt Mehlhorn. Algorithmic of Matching Under Preferences. , 2013. Internet resource.

Newman, Michael. Biometrics: Application, Technology, & Management. Boston, Mass: Course Technology, 2009. Print

Sally, Lin. Advances in Computer Science and Information Engineering: Volume 2. Springer Berlin Heidelberg, 2012. Internet resource.

May 02, 2023

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