Slave Swarms Research Articles

Parallel multi-swarm cooperative particle swarm optimization for protein–ligand docking and virtual screening

BackgroundA high-quality docking method tends to yield multifold gains with half pains for the new drug development. Over the past few decades, great efforts have been made for the development of novel docking programs with great efficiency and intriguing accuracy. AutoDock Vina (Vina) is one of these achievements with improved speed and accuracy compared to AutoDock4. Since it was proposed, some of its variants, such as PSOVina and GWOVina, have also been developed. However, for all these docking programs, there is still large room for performance improvement.ResultsIn this work, we propose a parallel multi-swarm cooperative particle swarm model, in which one master swarm and several slave swarms mutually cooperate and co-evolve. Our experiments show that multi-swarm programs possess better docking robustness than PSOVina. Moreover, the multi-swarm program based on random drift PSO can achieve the best highest accuracy of protein–ligand docking, an outstanding enrichment effect for drug-like activate compounds, and the second best AUC screening accuracy among all the compared docking programs, but with less computation consumption than most of the other docking programs.ConclusionThe proposed multi-swarm cooperative model is a novel algorithmic modeling suitable for protein–ligand docking and virtual screening. Owing to the existing coevolution between the master and the slave swarms, this model in parallel generates remarkable docking performance. The source code can be freely downloaded from https://github.com/li-jin-xing/MPSOVina.

An improved PSO method for optimal design of subsea oil pipelines

The construction cost of subsea oil pipelines takes rather high portion in the overall developing investment in offshore oilfield, thus an effective design of subsea oil pipelines is one of the main measurements to decrease the cost in offshore oilfield. In this paper, a new method for design and optimization of subsea pipelines is proposed, which conjunctly analyzes thermal-hydraulic performance and structural integrity, and uniformly solves main parameters for pipeline design, such as internal diameter, wall thickness, insulation layer thickness and pipeline inlet pressure and temperature. A complex non-linear mathematical model is established to minimize the total construction investment and operation costs while ensuring that the system meets minimum structural integrity standards and operation requirements over its anticipated service life; the model also analyzes the uncertainties in operation parameters, such as environmental temperature, overburden pressure and delivery flow fluctuations. Four multi-swarm cooperative improved particle swarm optimization algorithms (MC-GPSO, MC-LPSO, MC-FIPSO and MC-SLPSO) are employed for calculation. Monte Carlo method is introduced into the algorithms to determine the uncertain parameters. Through comparison, MC-SLPSO algorithm can optimally solve this type of model when 5 slave swarms are employed. Finally, a virtual oil pipeline and a practical multiphase subsea pipeline are set as two examples to demonstrate the performance of the optimization schemes.

Particle Swarm Optimization with Double Learning Patterns.

Particle Swarm Optimization (PSO) is an effective tool in solving optimization problems. However, PSO usually suffers from the premature convergence due to the quick losing of the swarm diversity. In this paper, we first analyze the motion behavior of the swarm based on the probability characteristic of learning parameters. Then a PSO with double learning patterns (PSO-DLP) is developed, which employs the master swarm and the slave swarm with different learning patterns to achieve a trade-off between the convergence speed and the swarm diversity. The particles in the master swarm and the slave swarm are encouraged to explore search for keeping the swarm diversity and to learn from the global best particle for refining a promising solution, respectively. When the evolutionary states of two swarms interact, an interaction mechanism is enabled. This mechanism can help the slave swarm in jumping out of the local optima and improve the convergence precision of the master swarm. The proposed PSO-DLP is evaluated on 20 benchmark functions, including rotated multimodal and complex shifted problems. The simulation results and statistical analysis show that PSO-DLP obtains a promising performance and outperforms eight PSO variants.

Multi-Agent Based Distributed Computing Framework for Master-Slave Particle Swarms

面向大规模复杂优化问题,提出了一个基于并行粒子群优化的分布式Agent 计算框架.框架中使用一个主群(master swarm)来演化问题的完整解,并使用一组从群(slave swarm)来并行优化一组子问题的解,主群和从群通过交替执行来提高问题的求解效率.采用异步组结构,主群/从群中的各类Agent 共享一个解群,并通过相互协作,对解群进行构造、改进、修补、分解和合并等演化操作.该框架可用于求解复杂的约束多目标优化问题.通过一类典型运输问题上的实验,其结果表明,所提出的方法明显优于另外两种先进的演化算法.

A two-swarm cooperative particle swarms optimization

Particle swarm optimization (PSO) is a powerful stochastic evolutionary algorithm based on swarm intelligence. However, the canonical PSO is easily and prematurely trapped in the local optima due to loss of its diversity. Although some improved algorithms retain the diversity, their speeds of convergence become slow. Meanwhile, PSO could only find out the global optimum in a small search interval, which greatly limits its applications to many practical engineering problems. In this study, the two-swarm cooperative particle swarm optimization (TCPSO) is presented. TCPSO can not only catch the global optimum in a large search space such as 2×1010, but also obtains a good balance between the swarm diversity and the convergence speed. It uses two particle swarms, the slave swarm and the master swarm with the clear division of their works. The former particles are updated without using the current velocities, the dimension of each particle learns from the same dimension of its neighboring particle instead of the best-so-far position. These features make the particles of the slave swarm concentrate toward the local optimum, thus accelerating the convergence. The latter particles are updated based on the former particles. And the equation in which the velocities of its particles are updated uses a large inertia weight. The feature of the master swarm keeps its diversity invariant. The experiments on TCPSO through 14 test functions showed that it significantly improves the performance of PSO and possesses the best performance among all the examined problems no matter multimodal or unimodal functions.

Handling multi-objective optimization problems with a multi-swarm cooperative particle swarm optimizer

This paper presents a new multi-objective optimization algorithm in which multi-swarm cooperative strategy is incorporated into particle swarm optimization algorithm, called multi-swarm cooperative multi-objective particle swarm optimizer (MC-MOPSO). This algorithm consists of multiple slave swarms and one master swarm. Each slave swarm is designed to optimize one objective function of the multi-objective problem in order to find out all the non-dominated optima of this objective function. In order to produce a well distributed Pareto front, the master swarm is developed to cover gaps among non-dominated optima by using a local MOPSO algorithm. Moreover, in order to strengthen the capability locating multiple optima of the PSO, several improved techniques such as the Pareto dominance-based species technique and the escape strategy of mature species are introduced. The simulation results indicate that our algorithm is highly competitive to solving the multi-objective optimization problems.

Improved particle swarm algorithm for hydrological parameter optimization

In this paper, a new method named MSSE-PSO (master–slave swarms shuffling evolution algorithm based on particle swarm optimization) is proposed. Firstly, a population of points is sampled randomly from the feasible space, and then partitioned into several sub-swarms (one master swarm and other slave swarms). Each slave swarm independently executes PSO or its variants, including the update of particles’ position and velocity. For the master swarm, the particles enhance themselves based on the social knowledge of master swarm and that of slave swarms. At periodic stage in the evolution, the master swarm and the whole slave swarms are forced to mix, and points are then reassigned to several sub-swarms to ensure the share of information. The process is repeated until a user-defined stopping criterion is reached. The tests of numerical simulation and the case study on hydrological model show that MSSE-PSO remarkably improves the accuracy of calibration, reduces the time of computation and enhances the performance of stability. Therefore, it is an effective and efficient global optimization method.

A multi-swarm optimizer based fuzzy modeling approach for dynamic systems processing

Inspired by the phenomenon of symbiosis in natural ecosystems a multi-swarm cooperative particle swarm optimizer (MCPSO) is proposed as a new fuzzy modeling strategy for identification and control of non-linear dynamical systems. In MCPSO, the population consists of one master swarm and several slave swarms. The slave swarms execute particle swarm optimization (PSO) or its variants independently to maintain the diversity of particles, while the particles in the master swarm enhance themselves based on their own knowledge and also the knowledge of the particles in the slave swarms. With four benchmark functions, MCPSO is proved to have better performance than PSO and its variants. MCPSO is then used to automatically design the fuzzy identifier and fuzzy controller for non-linear dynamical systems. The proposed algorithm (MCPSO) is shown to outperform PSO and some other methods in identifying and controlling dynamical systems.

MCPSO: A multi-swarm cooperative particle swarm optimizer

This paper presents a new optimization algorithm – MCPSO, multi-swarm cooperative particle swarm optimizer, inspired by the phenomenon of symbiosis in natural ecosystems. MCPSO is based on a master–slave model, in which a population consists of one master swarm and several slave swarms. The slave swarms execute a single PSO or its variants independently to maintain the diversity of particles, while the master swarm evolves based on its own knowledge and also the knowledge of the slave swarms. According to the co-evolutionary relationship between master swarm and slave swarms, two versions of MCPSO are proposed, namely the competitive version of MCPSO (COM-MCPSO) and the collaborative version of MCPSO (COL-MCPSO), where the master swarm enhances its particles based on an antagonistic scenario or a synergistic scenario, respectively. In the simulation studies, several benchmark functions are performed, and the performances of the proposed algorithms are compared with the standard PSO (SPSO) and its variants to demonstrate the superiority of MCPSO.