International Journal of Innovative Computing and Applications (9 papers in press)

• Using selection to improve Quantum-behaved Particle Swarm Optimization
  by Haixia Long, Jun Sun, Wenbo Xu 
  Abstract: Quantum-behaved particle swarm optimization (QPSO) algorithm is a global convergence guaranteed algorithms, which outperforms original PSO in search ability but has fewer parameters to control. This paper describes two selection mechanisms into QPSO to improve the search ability of QPSO: QPSO with tournament selection (QPSO-TS) and QPSO with roulette-wheel selection (QPSO-RS). While the center of position distribution of each particle in QPSO is determined by global best position and personal best position, in the QPSO with selection operation, the global best position is substituted by a candidate solution through selection. The QPSO with selection operation also maintains the mean best position of the swarm as in the previous QPSO to make the swarm more efficient in global search. The experiment results on benchmark functions show that QPSO-RS has stronger global search ability than QPSO-TS, QPSO and standard PSO.
  Keywords: QPSO; Tournament selection; Roulette-wheel selection; Global best position; QPSO-TS; QPSO-RS
   
• A parallel hybrid Ant-tabu algorithm for integrated emergency vehicle dispatching and covering problem
  by Sarah Ibri, Habiba Drias, Mustapha Nourelfath 
  Abstract: In this paper we propose, implement and analyze a parallel solution to solve an integrated dispatching and covering problem for emergency vehicle fleet management system. The basic sequential algorithm is a coupled Ant Colony System (ACS) with Tabu Search heuristic. To speed up this algorithm, we develop a master slave ACS parallel version based on the parallel neighbourhood evaluation approach for the tabu procedure. In experiments we compare synchronization strategies between the parallel processes and show the impact of inter processes communication frequency and the information exchanged on the efficiency of the algorithm.
  Keywords: parallel algorithms, optimization, emergency vehicle planning, Ant colony, tabu search, real time, dispatching and covering problems.
   
• A dynamic vehicle routing problem based on real-time traffic information
  by Xin Zhao, Gilles Goncalves, Rémy Dupas 
  Abstract: We treat the dynamic vehicle routing problem with time windows (DVRPTW) in the context of real-time traffic information. We integrate traffic information obtained in real time to change the speed profile according to the accidents of the road network (congestion, etc.). The travel times are based on a time-dependent model in which the travel speeds are step functions. This model is enriched with an exponential smoothing function able to calculate the forecasted speed. The analysis of the results of these experiments shows that our method with real-time traffic information provides a good performance, a better robustness against a simple model with time dependent travel time.
  Keywords: DVRPTW; time-dependent travel time; real-time traffic information; genetic algorithm
   
• Individual Predicted Integral-controlled Particle Swarm Optimization
  by Xingjuan Cai 
  Abstract: Integral-controlled particle swarm optimization (ICPSO) is an effective variant of particle swarm optimization family aiming to increase the population diversity. However, the performance of ICPSO is heavily relied upon the values of cognitive learning factor and social learning factor. The linear selection manner of ICPSO may not work well in many cases due to the complex nature of the optimization problems. Since the large cognitive coefficient provides a large local search capability, whereas the small one employs a large global search capability, a new variant -- individual predicted integral-controlled particle swarm optimization is proposed in which the social and cognitive learning factors are adjusted according to a predefined predicted velocity index. If the average velocity of one particle is superior to the index, its social and cognitive parameters will chose a convergent setting, and vice versa. Simulation results show the proposed variant is more effective and efficient than other three variants of particle swarm optimization when solving multi-modal high-dimensional numerical problems.
  Keywords: Integral-controlled particle swarm optimization; quadratic interpolation method; local optima.
   
• Optimal Ballast Scheme Design for Cargo Ships Using an Improved Genetic Algorithm
  by Jing Chen, Yan Lin 
  Abstract: Loading design is a key stage in ship’s design process. It directly affects a ship’s stability, strength, and attitude at sea. The loading design problem contains many mutual conflicting requirements and belongs to a multi-objective combinational optimization problem with multiple constraints. In this paper, based on the complex technical requirements of ship’s loading design, a multi-objective optimization mathematical model was constructed. In the model, the overall longitudinal strength and the intact stability performances were taken as the objectives, and the limits of draughts, ship’s attitude, stability and strength, etc. were taken as the constraints. A strategy of tanks being Fully-Loaded-First was used and the corresponding improved genetic algorithm was presented. In the algorithm, the multi-objective constrained combinational optimization problem is transformed into a single objective problem by using the weighted sum approach. A novel heuristic dynamic adjusting rule was proposed to dynamically adjust the normalizing coefficients of the objective functions to balance the orders of the magnitude of the objective values. An instance of the loading problem of a 50000DWT double hull product tanker was presented. The simulation results showed that the proposed method of combining the mathematical model and the improved GA was efficient in providing optimal solutions that are good for engineering application, and superior to the traditional human experience method.
  Keywords: ship loading design, multi-objective combinational optimization, genetic algorithm, longitudinal strength, stability.
   
• On mass effects to artificial physics optimization algorithm for global optimization problems
  by Liping Xie 
  Abstract: Artificial Physics Optimization (APO) algorithm is an optimization algorithm based on Physicomimetics framework. Driven by virtual force, a population of sample individuals searches a global optimum in the problem space. The mass of each individual corresponds to a user-defined function of the value of an objective function to be optimized. It is an important parameter to influence the performance of APO algorithm. Therefore, in this paper, the authors make a study on the selection principle of mass on numerical optimization problems. According to the curvilinear style of the mass functions, they are classified into three different types of curvilinear functions: convex function, linear function and concave function. To make a deep insight, several versions of APO algorithm with different mass functions are used to solve two type benchmarks: unimodal and multimodal functions. Simulation results show the mass functions with concave curve may generally obtain the satisfied solution within the allowed iterations. In addition, the performance of APO algorithm is compared with that of the modified Electromagnetism-like (EM), differential evolution (DE), evolutionary algorithm (EA) and particle swarm optimization (PSO) for multidimensional numeric benchmarks. The simulation results show that APO algorithm is competitive.
  Keywords: Physicomimetics; Artificial Physics Optimization; global optimization; virtual force; Newton’s Second law
   
• Barebones particle swarm for multi-objective optimization problems
  by Yong Zhang, Dun-wei Gong, Ya-nan Jiang 
  Abstract: Control parameters, inertia weight and acceleration coefficients influence strongly performance of multi-objective particle swarm optimization (MOPSO) algorithms. To eliminate the need for tuning of these parameters for different optimization problems, this paper presents an almost parameter-free MOPSO algorithm, in which the concept of barebones particle swarm is incorporated into MOPSO. A special mutation operator that enriches the exploratory capabilities of our algorithm is also introduced. The proposed algorithm is validated using several benchmark test problems and four standard metrics. Results indicate that the proposed algorithm is highly competitive, and that can be considered a viable alternative to solving multi-objective optimization problems.
  Keywords: multi-objective optimization; particle swarm optimization; barebones particle swarm optimization; mutation operator.
   
• Boid Particle Swarm Optimization
  by Zhihua Cui 
  Abstract: Particle swarm optimization (PSO) is a novel population-based stochastic optimization algorithm inspired by the Reynolds’ boid model. The original biological background of boid obeys three basic simple steering rules: separation, alignment and cohesion. However, to promote a simple update equation, none of these rules of boid model is employed by PSO methodology. Due to the weakness of biological background of PSO, in this paper, a new variant of PSO, boid particle swarm optimization (BPSO), is designed in which cohesion rule and alignment rule are both employed to improve the performance. In BPSO, each particle has two motions: divergent motion and convergent motion. For divergent motion, each particle adjusts its moving direction according to the the alignment direction and the cohesion direction, as well as in convergent motion, the original update equation of the standard version of PSO is used. To make a motion transition, a threshold is introduced to make the divergent motion is employed in the first period, whereas the convergent motion is used in the final stage. To testify the efficiency, several unconstrained benchmarks are used to compare. Simulation results show the proposed variant is more effective and efficient than other two variants of particle swarm optimization when solving multi-modal highdimensional numerical problems.
  Keywords: Boid particle swarm optimization; separation rule; alignment rule; cohesion rule
   
• Transgenetic Algorithm for the Periodic Mobile Piston Pump Unit Routing Problem with Continuous Oil Replenishment
  by Marco Goldbarg, Elizabeth Goldbarg, Herbert Duarte 
  Abstract: This paper reports the application of a transgenetic algorithm to the Periodic Mobile Piston Pump Unit Routing Problem with Continuous Oil Replenishment Problem, a complex problem that is applied in the exploitation of onshore oil fields. The transgenetic algorithms are based on endosymbiosis and on mechanisms of genetic exchange that occur in the intracellular flow. A computational experiment is reported to validate the performance of the proposed approach. In view of the difficulty in obtaining exact solutions to the problem, an upper limit is proposed. Aiming to bring the cases tackled in the computational experiment to operating conditions of onshore fields, a set of instances is built under realistic conditions of work.
  Keywords: Periodic Mobile Piston Pump Unit Routing Problem; Transgenetic Algorithm; Evolutionary Algorithm; Routing; Scheduling