Multi-strategy based bare bones particle swarm for numerical optimisation

The bare bones particle swarm optimization algorithm is a useful method for the optimization problems. Each individual particle has been given memories to recorded its personal best position. The best of all personal best positions is recorded as the global best position by the particle swarm. A Gaussian distribution is used to control the behavior of the particles according to the personal and the global best position. However, this iterative pattern weak at multimodal problems. Particles are easy to be trapped in the local minimums. To cross this shortcoming, the dynamic reconstruction bare bones particle swarm optimization algorithm (DRBBPSO) is proposed in this work. The dynamic reconstruction strategy is used to enhance the global search ability of the particle swarm. Numbers of elite particles are selected to reconstruct the particle swarm. To verify the performance of the proposed algorithm, a set of comprehensive benchmark functions are used in the experiments. Also, several swarm-based algorithms including the standard bare bone particle swarm optimization algorithm are used in the control group. The experimental results confirmed the searching ability of the DRBBPSO.

Multi swarm bare bones particle swarm optimization with distribution adaption

The bare bones particle swarm optimization (BBPSO) algorithm has been proved to be an effective tool for single-objective optimization problems over continuous search spaces. In this paper, a fission-fusion hybrid bare bones particle swarm optimizer (FHBBPSO) is proposed. The FHBBPSO combines a fission strategy and a fusion strategy to sample new positions of the particles. The fission strategy aims at splitting the search space. Particles are assigned to different local groups to sample the corresponding regions. On the other side, the fusion strategy aims at narrowing the search space. Marginal groups will be gradually merged by the central groups until there is only one group left. The two strategies work together for the theoretical optimal value. To confirm the searching ability of the FHBBPSO, the proposed method runs over the IEEE Congress on Evolutionary Computation 2014 (CEC2014) benchmark functions. Also, several famous evolutionary methods are used in the control group. The experimental results and analysis suggest that the FHBBPSO is a highly competitive optimization algorithm for single-objective functions.

Particle swarm optimization (PSO) is a new intelligent search technique, which is inspired by swarm intelligence. Although PSO has shown good performance in many benchmark optimization problems, it suffers from premature convergence in solving complex multimodal problems. In this paper, we propose a novel PSO algorithm, called PSO with a simulated binary crossover operator (SCPSO), to improve the performance of PSO. Experimental results on several benchmark problems show that SCPSO achieves better performance than standard PSO. http://dx.doi.org/10.11591/telkomnika.v12i12.5999

Concave cost transhipment problems are difficult to optimally solve for large-scale problems within a limited period of time. Recently, some modern meta-heuristics have been employed for the development of advanced local search based or population-based stochastic search algorithms that can improve the conventional heuristics. Besides these meta-heuristics, the ant colony system algorithm is a population-based stochastic search algorithm which has been used to obtain good results in many applications. This study employs the ant colony system algorithm, coupled with some genetic algorithm and threshold accepting algorithm techniques, to develop a population based stochastic search algorithm for efficiently solving square root concave cost transhipment problems. The developed algorithms are evaluated with a number of problem instances. The results indicate that the proposed algorithm is more effective for solving square root concave cost transhipment problems than other recently designed local search based algorithms and genetic algorithm.

A twinning bare bones particle swarm optimization(TBBPSO) algorithm is proposed in this paper. The TBBPSO is combined by two operators, the twins grouping operator (TGO) and the merger operator (MO). The TGO aims at the reorganization of the particle swarm. Two particles will form as a twin and influence each other in subsequent iterations. In a twin, one particle is designed to do the global search while the other one is designed to do the local search. The MO aims at merging the twins and enhancing the search ability of the main group. Two operators work together to enhance the local minimum escaping ability of proposed methods. In addition, no parameter adjustment is needed in TBBPSO, which means TBBPSO can solve different types of optimization problems without previous information or parameter adjustment. In the benchmark functions test, the CEC2014 benchmark functions are used. Experimental results prove that proposed methods can present high precision results for various types of optimization problems.

Neural Network system for SoC is one topic in the world. Recently, SoC began to combine AI technique by Python. When developers consider Intelligent system on SoC, the co-design software and hardware are significant. In this case, small devices and embedded system are target system. For IoT, those techniques will grow up in the world. Those cases, avoiding premature convergence while keeping performance is a challenge in training the neural network, especially in a case of large NNs or a large number of training data. In this case, one problem is a huge memory and processing power. For tiny memory and processing system as SoC, we propose an improved particle swarm optimization (PSO) algorithm called the PSOseed2 algorithm for training NN. The PSOseed2 algorithm solves the premature convergence of the standard PSO (SPSO) algorithm by slightly modifying the velocity update function without adding many computational tasks to the SPSO algorithm. We evaluated this algorithm on field programmable gate array (FPGA)-based NN and software-based NN and trained these NNs with different PSO algorithms that are SPSO, PSOseed, PSOseed2, and dissipative PSO. Experimental results with different datasets confirmed that the NNs trained by the proposed PSOseed2 algorithm had better recognition rates and lower global learning errors than the NN trained by other PSO algorithms. In this talk, I would like to talk those relational research in the world and our results and discussion.

The standard particle swarm optimization algorithm (simply called PSO) has many advantages such as rapid convergence. However, a major disadvantage confronting the PSO algorithm is that they often converge to some local optimization. In order to avoid the occurrence of premature convergence and local optimization of the PSO algorithm, a particle swarm optimization algorithm based on genetic selection stra-tegy, simply called GSS-PSO, is singled out in this paper. GSS-PSO not only retains the rapid convergence charactering of the standard PSO algorithms, but also scales up their global search ability. At last, we experimentally tested the efficiency of our new GSS-PSO algorithm using eight classical functions. The experimental results show that our new GSS-PSO algorithm is generally better than the PSO algorithm.KeywordsPSOConvergenceFunction optimizationGSS

In order to improve the stability and sensitivity of particle swarm optimization (PSO) algorithm and to solve the problem of premature convergence, in this re-search, a computer PSO algorithm based on thermodynamic motion mechanism is proposed based on the principle of thermodynamic motion mechanism. Firstly, the thermodynamic motion phenomenon, the diffusion law in kinematics and the standard PSO algorithm are introduced. Then, according to the basic idea of thermodynamic motion mechanism, the standardized PSO algorithm is optimized and its optimization process is introduced. Finally, the experimental results are analysed by setting the test function. The results show that among the five test functions, the computer PSO algorithm based on thermodynamic motion mechanism has a higher probability of jumping out of the local optimal solution. Its robustness and stability are much better than standard PSO algorithms. The evolution ability of the computer PSO algorithm based on thermodynamic motion mechanism is better than that of the standard PSO algorithm. The standard PSO algorithm is superior because it is based on thermodynamic motion mechanism. The research in this paper can provide good guidance for improving the performance of PSO algorithm.

Sparse Code Multiple Access (SCMA) is a class of competitive non-orthogonal multiple access (NOMA) that is proposed to support massive Machine Type of Communication (m-MTC). In this paper, we propose a new scheme to construct the SCMA codebooks, where we jointly optimize the rotation angles and mother codebook parameters. The basic idea is to make the upper bound of the error probability as smaller as possible. To realize it, we search for suitable angles and parameters through the bare bones particle swarm optimization (BBPSO) algorithm. The simulation results demonstrate that compared with the original SCMA, Star-QAM SCMA and LDS the new proposed SCMA codebooks provides better performance in both additive white Gaussian noise (AWGN) and Rayleigh channels, moreover, it also gives better performance in high overloading.

This paper proposes novel heuristic algorithm called gravitational search algorithm (GSA) to speech enhancement. Stochastic and heuristic algorithms like particle swarm optimization (PSO) and some of its variants have been adapted to the field of speech enhancement in recent years. Although standard PSO (SPSO) finds good solutions, it suffers from the premature convergence by getting trapped into local optimum. In order to increase the diversity in search space and to improve the local searching capability, another recently developed heuristic algorithm GSA is proposed to speech enhancement in this paper. GSA is mainly constructed on the basis of law of gravity and the notion of mass interactions .The proposed algorithm is studied for real world noise condition called babble noise, at three different input SNR levels. To the best of our knowledge, there is no analysis about the intelligibility of enhanced speech using optimization techniques. In the present study, the proposed algorithm is compared with the standard PSO (SPSO) algorithm for dual channel speech enhancement, and the intelligibility analysis is also reported. Simulation results indicate that GSA-based algorithm outperforms the particle swarm optimization in adaptive noise cancellation with an improved speech quality and intelligibility.

In our previous work, a new PSO algorithm called θPSO based on phase angle was put forward, which has better optimization performance than standard PSO algorithm when dealing with some simple benchmark functions. But this algorithm may easily stick in the local minima when handling some complex or multimode functions. In this paper, an improved θ-PSO with mutation operator is studied. And this improved algorithm has better optimization performance when solving some complex benchmark functions. Benchmark testing shows that this improved θ-PSO algorithm can overcome the local minima and achieve the goal of global minimum in limited iterations.

In this paper, a hybrid algorithm based on Particle Swarm Optimization (PSO) and Cuckoo Search (CS) is detailed with the application of Optimal Reactive Power Dispatch (ORPD). The Levy flight and the elimination mechanism are introduced to the traditional PSO algorithm. Benchmark tests show that the hybrid algorithm has better optimization efficiency than original PSO and CS algorithms. In the application of the ORPD, the fitness function is the total real loss of the distribution network. Inequality constrains are handled by the limitation of searching space. The simulation results on the IEEE-14 bus standard system and a real distribution network illustrate the feasibility and effectiveness of the algorithm. The total real power losses decrease 7.1% and 8% in two applications. The mathematical tests and engineering applications demonstrate that the hybrid algorithm has better searching ability than the standard PSO algorithm, and it is suitable for the solution of ORPD problem.

Particle Swarm Optimization (PSO) is a newly appeared technique for evolutionary computation. It was originated as a simulation for a simplified social system such as the behavior of bird flocking or fish schooling. An improved PSO algorithm (IPSO) is introduced to solve the nonlinear optimization for indoor positioning. The algorithm achieves the optimal coordinates through iterative searching. Compared with standard PSO algorithm, the algorithm converges faster and can find the global best position. The error of position estimated by this algorithm is smaller than that estimated in Taylor Series Expansion (TSE) and Genetic Algorithm (GA). Thus this algorithm is proven to be a fast and effective method in solving nonlinear optimization for indoor positioning.

This paper presents a hybrid approach based discrete Particle Swarm Optimization (PSO) and chaotic strategies for solving multi-objective task scheduling problem in cloud computing. The main purpose is to allocate the summited tasks to the available resources in the cloud environment with minimum makespan (i.e. schedule length) and processing cost while maximizing resource utilization without violating Service Level Agreement (SLA) among users and cloud providers. The main challenges faced by Particle Swarm Optimization (PSO) when used to solve scheduling problems are premature convergence and trapping into local optimum. This paper presents an enhanced Particle Swarm Optimization algorithm hybridized with Chaotic Map strategies. The proposed approach is called Enhanced Particle Swarm Optimization based Chaotic Strategies (EPSOCHO) algorithm. Our proposed approach suggests two Chaotic Map strategies: sinusoidal iterator and Lorenz attractor to enhanced PSO algorithm in order to get good convergence and diversity for optimizing the task scheduling in cloud computing. The proposed approach is simulated and implemented in Cloudsim simulator. The performance of the proposed approach is compared with the standard PSO algorithm, the improved PSO algorithm with Longest job to fastest processor (LJFP-PSO), and the improved PSO algorithm with minimum completion time (MCT-PSO) using different sizes of tasks and various benchmark datasets. The results clearly demonstrate the efficiency of the proposed approach in terms of makespan, processing cost and resources utilization.