Simulated annealing method for equilibrium placement problem

The paper proposes a modification of the simulated annealing algorithm as applied to problems that have a fragmented structure. An algorithm for simulating annealing for the traveling salesman problem is considered and its applicability to the optimization problem on a set of permutations is shown. It is proved that the problem of equilibrium placement of point objects on a plane has a fragmentary structure and, therefore, reduces to an optimization problem on a set of permutations. The results of numerical experiments for various types of algorithms for finding the optimal solution in the equilibrium placement problem are presented.

A New Memetic Approach to Solve the Strategic Berth Template Problem

The berth allocation problem is one of the main concerns of port operators at a container terminal. In this paper, the authors study the berth allocation problem at the strategic level commonly known as the strategic berth template problem (SBTP). This problem aims to find the best berth template for a set of calling ships accepted to be served at the port. At strategic level, port operator can reject some ships to be served for avoid congestion. Since the computational complexity of the mathematical formulation proposed for SBTP, solution approaches presented so far for the problem are limited especially at level of large-scale instances. In order to find high quality solutions with a short computational time, this work proposes a population based memetic algorithm which combine a first-come-first-served (FCFS) technique, two genetics operators, and a simulating annealing algorithm. Different computational experiences and comparisons against the best known solutions so far have been presented to show the performance and effectiveness of the proposed method.

Enhanced Simulating Annealing and SVM for Intrusion Detection System in Wireless Sensor Networks

Wireless Sensor Networking (WSN) is among the most recent technologies with uses ranging from medicine to the military. Nevertheless, WSNs are impervious to numerous types of cyber-attacks that could compromise the performance of the entire network, which could lead to fatal problems such as a routing attacks, denial-of-service attack, probe, etc. Key management protocols, secure routing, and authentication protocols cannot offer WSN protections for such kinds of attacks. The intrusion detection scheme is the way to solve the issue. This paper proposes an Enhanced simulated annealing based support vector machine algorithm for intrusion detection. Traditional features selection algorithm simulating annealing takes much time to run. So, to avoid this problem, we have introduced Enhanced simulated annealing. From the performance results, it can be seen that our proposed feature selection method provides better performance results than the existing method.

Growth, Mortality and Exploitation Rates of African Moonfish (Selene dorsalis, Gill 1863) Encountered in the Coast of Ghana (West Africa)

Growth, mortality and exploitation rate of Selene dorsalis (Gill, 1863) from the continental shelf of Ghana (West Africa) were examined between July 2018 and June 2019. The study provided results on fishery dynamics parameters needed to estimate the stock status and characteristics of S. dorsalis in the coast of Ghana. Monthly length-frequency data were collected from 629 samples and analysed using fisheries models fitted in TropFishR package in R software. The von Bertalanffy growth parameters were utilised to analyse the population dynamics of the species using ELEFAN Simulating Annealing. Based on the estimates, the asymptotic total length (L∞) was 22.2 cm, the coefficient of growth (K) was 0.76 year-1, and the calculated growth performance index (phi) was 2.58 with Rn value of 0.55. The total mortality rate (Z) was 3.32 year-1 with a natural mortality rate (M) of 1.21 year-1 and fishing mortality rate (F) of 2.11 year-1. The exploitation rate (E) estimated for the species was above the optimum level of 0.5, which indicates that S. dorsalis is overexploited in the coast of Ghana. It can be concluded that the exploitation rate of S. dorsalis has exceeded the optimum limit, hence the need for enforcement and improvement of fisheries management measures such as mesh size regulations, capping of canoes, closed fishing seasons and compliance with fisheries policies.

Parallel machines scheduling with time-dependent deterioration, using meta-heuristic algorithms

In this paper, we consider the identical parallel machines scheduling problem with exponential time-dependent deterioration. The meaning of time-dependent deterioration is that the processing time of a job is not a constant and depends on the scheduled activities. In other words, when a job is processed later, it needs more processing time compared to the jobs processed earlier. The main purpose is to minimize the makespan. To reach this aim, we developed a mixed integer programming formulation for the problem. We solved problem in small scale using GAMS software, while due to the fact that in larger scales the aforesaid case is a complex and intricate optimized problem which is NP-hard, it is not possible to solve it by standard calculating techniques (in logical calculating times); we applied the meta-heuristic genetic algorithm, simulating annealing and artificial immune system, and their performance has been evaluated. In the end, we showed that solving the problem in small scale, with the meta-heuristic algorithms (GA, SA, and AIS) equals the optimal solution (GAMS), And on a large scale, at a time of approximately equal solution, meta-heuristic algorithm simulating annealing, provides a more optimal solution.

Scalable THz Network-On-Chip Architecture for Multichip Systems

While THz wireless network-on-chip (WiNoC) introduces considerably high bandwidth, due to the high path loss, it cannot be used for communication between far apart nodes, especially in a multichip architecture. In this paper, we introduce a cellular and scalable architecture to reuse the frequencies of the chips. Moreover, we use a novel structure called parallel-plate waveguide (PPW) that is suitable for interchip communication. The low-loss property of this waveguide lets us increase the number of chips. Each chip has a wireless node as a gateway for communicating with other chips. To shorten the length of intra- and interchip THz links, the optimum configuration is determined by leveraging the multiobjective simulating annealing (SA) algorithm. Finally, we compare the performance of the proposed THz multichip NoC with a conventional millimeter-wave one. Our simulation results indicate that when the system scales up from four to sixteen chips, the throughput of our design is decreased about 5.8 % , while for millimeter-wave NoC, this reduction is about 21 % . Furthermore, the average latency growth of our system is only 1 % compared with about 40 % increase for the millimeter-wave NoC.

SOLVING N-QUEEN PROBLEM USING PROBABILITY COLLECTIVE

Many types of research solve N-Queen Problem by using various techniques as Genetic algorithm (GA), particle swarm optimisation (PSO), and simulating annealing (SA). This paper motivates and describes the use of probability collectives (PC) with coordination multi-agent system to solve the N-Queen Problem. The main challenge is to make the agents work in a coordinate a way, optimising the local utilities and contributing the maximum towards optimisation of the global objective. Keywords: Probability Collectives, Collective Intelligence, Multiagent systems, N-Queen Problem..

Comparative between optimization feature selection by using classifiers algorithms on spam email

<span lang="EN-US">Spam mail has become a rising phenomenon in a world that has recently witnessed high growth in the volume of emails. This indicates the need to develop an effective spam filter. At the present time, Classification algorithms for text mining are used for the classification of emails. This paper provides a description and evaluation of the effectiveness of three popular classifiers using optimization feature selections, such as Genetic algorithm, Harmony search, practical swarm optimization, and simulating annealing. The research focuses on a comparison of the effect of classifiers using K-nearest Neighbor (KNN), Naïve Bayesian (NB), and Support Vector Machine (SVM) on spam classifiers (without using feature selection) also enhances the reliability of feature selection by proposing optimization feature selection to reduce number of features that are not important.</span>