Hybrid Symbiotic Organisms Search Research Articles

Boosting symbiotic organism search algorithm with ecosystem service for dynamic blood allocation in blood banking system

ABSTRACT Blood is a valuable commodity in society due to its ability to save lives during crises. Furthermore, because of the scarcity of blood donors, blood assignment by blood banks requires meticulous planning and solid issuing policy. The multiple components of a blood banking system contribute to the complexity of maintaining an efficient structure for such a system. One particular aspect relates to the stochastic nature of the demand for blood units. This paper implements a mathematical model for a blood bank system in South Africa and additionally explores the possible implementation of a hybrid global optimisation metaheuristic approach for the efficient assignment of blood products in the blood bank system. The approximate optimisation method used is the hybridisation of the symbiotic organism search (SOS) algorithm and a pre-processing ecosystem services (PES) techniques. In order to show the practicability of the model and evaluate the accuracy and robustness of the newly proposed hybrid algorithm, several numerical computations were performed using synthetically generated datasets that fall within the initial blood volume bounds of 500 to 20, 000. The experimental results indicate that the hybrid symbiotic organisms search ecosystem services optimisation algorithm offers better solutions for blood allocation under a dynamic environment than does the standard symbiotic organism search algorithm and other previously proposed hybrid versions of the SOS methods.

Ant colony optimization for traveling salesman problem based on parameters optimization

Traveling salesman problem (TSP) is one typical combinatorial optimization problem. Ant colony optimization (ACO) is useful for solving discrete optimization problems whereas the performance of ACO depends on the values of parameters. The hybrid symbiotic organisms search (SOS) and ACO algorithm (SOS–ACO) is proposed for TSP. After certain parameters of ACO are assigned, the remaining parameters can be adaptively optimized by SOS. Using the optimized parameters, ACO finds the optimal or near-optimal solution and the complexity for assigning ACO parameters is greatly reduced. In addition, one simple local optimization strategy is incorporated into SOS–ACO for improving the convergence rate and solution quality. SOS–ACO is tested with different TSP instances in TSPLIB. The best solutions are within 2.33% of the known optimal solution. Compared with some of the previous algorithms, SOS–ACO finds the better solutions under the same preconditions. Finally, the performance of SOS–ACO is analyzed according to the changes of some ACO parameters. The experimental results illustrate that SOS–ACO has good adaptive ability to various values of these parameters for finding the competitive solutions.

Assembly sequence optimization based on hybrid symbiotic organisms search and ant colony optimization

Assembly sequence optimization aims to find the optimal or near-optimal assembly sequences under multiple assembly constraints. Since it is NP-hard for complex assemblies, the heuristic algorithms are widely used to find the optimal or near-optimal assembly sequences in an acceptable computation time. Considering the multiple assembly constraints, an assembly model is presented for assembly sequence optimization. Then, the hybrid symbiotic organisms search and ant colony optimization is used to find the optimal or near-optimal assembly sequences. The symbiotic organisms search has a relatively strong global optimization capability but weak local optimization capability. On the other hand, the ant colony optimization has the relatively strong local optimization capability for assembly sequence optimization even though the parameters are not optimized. The hybrid symbiotic organisms search and ant colony optimization take advantages of their capacities for assembly sequence optimization. The case study demonstrates that the hybrid symbiotic organisms search and ant colony optimization finds the better assembly sequences within less iteration than the individual ant colony optimization and symbiotic organisms search in most experiments under the same preconditions.

Enhanced symbiotic organisms search algorithm for unrelated parallel machines manufacturing scheduling with setup times

. This paper deliberates on the non-pre-emptive unrelated parallel machine scheduling problem with the objective of minimizing makespan. Machine and job sequence dependent set-up times are considered for the proposed scheduling methods, which are NP-hard, even without set-up times. The addition of sequence dependent setup times introduces additional complexity to the problem, which makes it very difficult to find optimal solutions, especially for large scale problems. Due to the NP-hard nature of the problem at hand, three different approaches are proposed to solve the problem including: An Enhanced Symbiotic Organisms Search (ESOS) algorithm, a Hybrid Symbiotic Organisms Search with Simulated Annealing (HSOSSA) algorithm, and an Enhanced Simulated Annealing (ESA) algorithm. A local search procedure is incorporated into each of the three algorithms as an improvement strategy to enhance their solution qualities. The computational experiments carried out showed that ESOS and HSOSSA performed better than the other methods on large problem instances with 12 machines and 120 jobs. The performance of each method is measured by comparing the quality of its solutions to the optimal solutions for the varying problem combinations. The results of the proposed methods are also compared with other techniques from the literature. Moreover, a comprehensive statistical analysis was performed and the results obtained show that the proposed algorithms significantly outperform the compared methods in terms of generality, quality of solutions, and robustness for all problem instances.

A hybrid symbiotic organisms search and simulated annealing technique applied to efficient design of PID controller for automatic voltage regulator

This article is motivated by incorporating a hybrid symbiotic organisms search and simulated annealing (hSOS-SA) technique into efficient design of PID controller for automatic voltage regulator (AVR). Symbiotic organism search (SOS) algorithm is contemplated first to optimize parameters of PID controller using a new cost function which considers both time-domain and frequency-domain specifications. The excellence of SOS over some state-of-the-art techniques is confirmed through transient response analysis, root locus analysis and bode analysis for the identical AVR system. To fine-tune controller parameters for enhancing the system stability margin further, simulated annealing algorithm is invoked subsequently at the instant SOS has converged. Extensive numerical results computed from time and frequency response specifications affirm the superiority of proposed hSOS-SA algorithm such that after a minimal overshoot, hSOS-SA tuned AVR system settles to the step reference quickly and follows it with the least steady-state error. Such response is found to ensure a better stability margin than that using original SOS and earlier studies. Finally, robustness analysis is realized to verify that the designed controller is robust with regard to parameter uncertainties.

A Hybrid Symbiotic Organisms Search Algorithm with Variable Neighbourhood Search for Solving Symmetric and Asymmetric Traveling Salesman Problem

Combinatorial optimization has been frequently used to solve both problems in science, engineering, and commercial applications. One combinatorial problems in the field of transportation is to find a shortest travel route that can be taken from the initial point of departure to point of destination, as well as minimizing travel costs and travel time. When the distance from one (initial) node to another (destination) node is the same with the distance to travel back from destination to initial, this problems known to the Traveling Salesman Problem (TSP), otherwise it call as an Asymmetric Traveling Salesman Problem (ATSP). The most recent optimization techniques is Symbiotic Organisms Search (SOS). This paper discuss how to hybrid the SOS algorithm with variable neighborhoods search (SOS-VNS) that can be applied to solve the ATSP problem. The proposed mechanism to add the variable neighborhoods search as a local search is to generate the better initial solution and then we modify the phase of parasites with adapting mechanism of mutation. After modification, the performance of the algorithm SOS-VNS is evaluated with several data sets and then the results is compared with the best known solution and some algorithm such PSO algorithm and SOS original algorithm. The SOS-VNS algorithm shows better results based on convergence, divergence and computing time.

Hybrid symbiotic organisms search algorithm for solving 0-1 knapsack problem

We propose a new binary version of hybrid symbiotic organisms search algorithm based on harmony search with greedy strategy for solving 0-1 knapsack problems. A greedy strategy is employed to repair the infeasible solution and optimise the feasible solution. The experiments are carried out in small-scale and large-scale knapsack problem instances. We report on computational experiments which are compared with the results achieved with other state-of-the-art approaches. The results attest the performance of our approach.

Hybrid Symbiotic Organisms Search for Optimal Fuzzified Joint Reconfiguration and Capacitor Placement in Electric Distribution Systems

Network reconfiguration and capacitor allocation are two valuable tools to optimally manage distribution systems. In the present work, an efficient algorithm is proposed for the joint problem of reconfiguration and capacitor placement in distribution systems. The multi-objective problem is formulated to minimize objective functions of total network power losses, bus voltage violation, and branch loading deviations subject to different technical and operational constraints. To enhance exploration ability, the Symbiotic Organisms Search algorithm is strengthened with Particle Swarm Optimization capabilities to develop the Hybrid Symbiotic Organisms Search algorithm, which is applied to solve the proposed problem. Objective functions are fuzzified and aggregated by a geometric mean operator to have the same scales. The proposed joint optimization problem is solved with the introduced Hybrid Symbiotic Organisms Search algorithm in two different case studies of balanced and unbalanced test systems. According to obtained results that are compared with some well-known evolutionary algorithms, the superiority of the Hybrid Symbiotic Organisms Search algorithm is confirmed in terms of solution optimality and convergence speed.

Correction: Hybrid Symbiotic Organisms Search Optimization Algorithm for Scheduling of Tasks on Cloud Computing Environment.

[This corrects the article DOI: 10.1371/journal.pone.0158229.].

Hybrid Symbiotic Organisms Search Optimization Algorithm for Scheduling of Tasks on Cloud Computing Environment.

Cloud computing has attracted significant attention from research community because of rapid migration rate of Information Technology services to its domain. Advances in virtualization technology has made cloud computing very popular as a result of easier deployment of application services. Tasks are submitted to cloud datacenters to be processed on pay as you go fashion. Task scheduling is one the significant research challenges in cloud computing environment. The current formulation of task scheduling problems has been shown to be NP-complete, hence finding the exact solution especially for large problem sizes is intractable. The heterogeneous and dynamic feature of cloud resources makes optimum task scheduling non-trivial. Therefore, efficient task scheduling algorithms are required for optimum resource utilization. Symbiotic Organisms Search (SOS) has been shown to perform competitively with Particle Swarm Optimization (PSO). The aim of this study is to optimize task scheduling in cloud computing environment based on a proposed Simulated Annealing (SA) based SOS (SASOS) in order to improve the convergence rate and quality of solution of SOS. The SOS algorithm has a strong global exploration capability and uses fewer parameters. The systematic reasoning ability of SA is employed to find better solutions on local solution regions, hence, adding exploration ability to SOS. Also, a fitness function is proposed which takes into account the utilization level of virtual machines (VMs) which reduced makespan and degree of imbalance among VMs. CloudSim toolkit was used to evaluate the efficiency of the proposed method using both synthetic and standard workload. Results of simulation showed that hybrid SOS performs better than SOS in terms of convergence speed, response time, degree of imbalance, and makespan.

Hybrid Symbiotic Organisms Search Algorithm for Solving 0-1 Knapsack Problem

We propose a new binary version of hybrid symbiotic organisms search algorithm based on harmony search with greedy strategy for solving 0-1 knapsack problems. A greedy strategy is employed to repair the infeasible solution and optimise the feasible solution. The experiments are carried out in small-scale and large-scale knapsack problem instances. We report on computational experiments which are compared with the results achieved with other state-of-the-art approaches. The results attest the performance of our approach.