Single Solution Algorithm Research Articles

Using Metaheuristics (SA-MCSDN) Optimized for Multi-Controller Placement in Software-Defined Networking

The multi-controller placement problem (MCPP) represents one of the most challenging issues in software-defined networks (SDNs). High-efficiency and scalable optimized solutions can be achieved for a given position in such networks, thereby enhancing various aspects of programmability, configuration, and construction. In this paper, we propose a model called simulated annealing for multi-controllers in SDN (SA-MCSDN) to solve the problem of placing multiple controllers in appropriate locations by considering estimated distances and distribution times among the controllers, as well as between controllers and switches (C2S). We simulated the proposed mathematical model using Network Simulator NS3 in the Linux Ubuntu environment to extract the performance results. We then compared the results of this single-solution algorithm with those obtained by our previously proposed multi-solution harmony search particle swarm optimization (HS-PSO) algorithm. The results reveal interesting aspects of each type of solution. We found that the proposed model works better than previously proposed models, according to some of the metrics upon which the network relies to achieve optimal performance. The metrics considered in this work are propagation delay, round-trip time (RTT), matrix of time session (TS), average delay, reliability, throughput, cost, and fitness value. The simulation results presented herein reveal that the proposed model achieves high reliability and satisfactory throughput with a short access time standard, addressing the issues of scalability and flexibility and achieving high performance to support network efficiency.

Global search in single-solution-based metaheuristics

PurposeMetaheuristic algorithms are classified into two categories namely: single-solution and population-based algorithms. Single-solution algorithms perform local search process by employing a single candidate solution trying to improve this solution in its neighborhood. In contrast, population-based algorithms guide the search process by maintaining multiple solutions located in different points of search space. However, the main drawback of single-solution algorithms is that the global optimum may not reach and it may get stuck in local optimum. On the other hand, population-based algorithms with several starting points that maintain the diversity of the solutions globally in the search space and results are of better exploration during the search process. In this paper more chance of finding global optimum is provided for single-solution-based algorithms by searching different regions of the search space.Design/methodology/approachIn this method, different starting points in initial step, searching locally in neighborhood of each solution, construct a global search in search space for the single-solution algorithm.FindingsThe proposed method was tested based on three single-solution algorithms involving hill-climbing (HC), simulated annealing (SA) and tabu search (TS) algorithms when they were applied on 25 benchmark test functions. The results of the basic version of these algorithms were then compared with the same algorithms integrated with the global search proposed in this paper. The statistical analysis of the results proves outperforming of the proposed method. Finally, 18 benchmark feature selection problems were used to test the algorithms and were compared with recent methods proposed in the literature.Originality/valueIn this paper more chance of finding global optimum is provided for single-solution-based algorithms by searching different regions of the search space.

Comparisons of metaheuristic algorithms for unrelated parallel machine weighted earliness/tardiness scheduling problems

This paper investigates unrelated parallel machine scheduling problems where the objectives are to minimize total weighted sum of earliness/tardiness costs. Three different metaheuristic algorithms are compared with others to determine what kind (swarm intelligence based, evolutionary or single solution) of metaheuristics is effective to solve these problems. In this study, artificial bee colony (ABC), genetic algorithm and simulated annealing algorithm are chosen as swarm intelligence based algorithm, evolutionary algorithm and single solution algorithm. All proposed algorithms are created without modification in order to determine effectiveness of these metaheuristics. Experimental results show that ABC outperforms its opponents in view of solution quality as swarm intelligence based metaheuristic algorithm.

HyperSPAM: A study on hyper-heuristic coordination strategies in the continuous domain

This article proposes a simplistic algorithmic framework, namely hyperSPAM, composed of three search algorithms for addressing continuous optimisation problems. The Covariance Matrix Adaptation Evolution Strategy (CMAES) is activated at the beginning of the optimisation process as a preprocessing component for a limited budget. Subsequently, the produced solution is fed to the other two single-solution search algorithms. The first performs moves along the axes while the second makes use of a matrix orthogonalization to perform diagonal moves.Four coordination strategies, in the fashion of hyperheuristics, have been used to coordinate the two single-solution algorithms. One of them is a simple randomized criterion while the other three are based on a success based reward mechanism. The four implementations of the hyperSPAM framework have been tested and compared against each other and modern metaheuristics on an extensive set of problems including theoretical functions and real-world engineering problems.Numerical results show that the different versions of the framework display broadly a similar performance. One of the reward schemes appears to be marginally better than the others. The simplistic random coordination also displays a very good performance. All the implementations of hyperSPAM significantly outperform the other algorithms used for comparison.

The Social Engineering Optimizer (SEO)

Although several meta-heuristics have been developed in the last two decades, most of them are population-based, undergo many steps along with several parameters that make them hard to understand and code. In addition, there are same procedures in recent metaheuristics which make them similar. So, the researchers usually are confused to select a metaheuristic and cannot find any superiority or at least in any algorithms. Because of this, the researchers still use the old algorithms instead of the recent ones. Contrary to previous work, this paper aims to develop a simple, intelligent and new single-solution algorithm that has just four main steps and three simple parameters to tune. Social Engineering Optimizer (SEO) starts with two initial solutions divided into attacker and defender. The attacker obtains the rules of Social Engineering techniques to reach its desired goals. By these simple features, the algorithm does precisely both intensification and diversification phases. The basis of the algorithm depends on how an attacker attacks to a defender by four different associated techniques. Finally, the proposed SEO is applied to solve a set of benchmark functions, important engineering and multi-objective optimization problems. The result shows its superiority in comparison with other well-known and recent meta-heuristics.

Multi-objective colliding bodies optimization algorithm for design of trusses

Abstract This article presents a new population-based optimization algorithm to solve the multi-objective optimization problems of truss structures. This method is based on the recently developed single-solution algorithm proposed by the present authors, so called colliding bodies optimization (CBO), with each agent solution being considered as an object or body with mass. In the proposed multi-objective colliding bodies optimization (MOCBO) algorithm, the collision theory strategy as the search process is utilized and the Maximin fitness procedure is incorporated to the CBO for sorting the agents. A series of well-known test functions with different characteristics and number of objective functions are studied. In order to measure the accuracy and efficiency of the proposed algorithm, its results are compared to those of the previous methods available in the literature, such as SPEA2, NSGA-II and MOPSO algorithms. Thereafter, two truss structural examples considering bi-objective functions are optimized. The performance of the proposed algorithm is more accurate and requires a lower computational cost than the other considered algorithms. In addition, the present methodology uses simple formulation and does not require internal parameter tuning. Highlights A new population-based algorithm is presented for multi-objective optimization. The algorithm is based on the recently developed single-solution colliding bodies optimization (CBO). The proposed multi-objective colliding bodies optimization is abbreviated as MOCBO. MOCBO utilizes the maximin fitness procedure for sorting the agents. A series of well-known test functions and number of objective functions are studied. The MOCBO is more accurate and requires lower computational cost. The MOCBO method uses simple formulation and requires no internal parameter tuning.