Modified Spider Monkey Optimization Algorithm Based on Self-Adaptive Inertia Weight

Spider monkey optimization (SMO) algorithm is a new swarm intelligence optimization algorithm proposed in recent years. It simulates the foraging behavior of spider monkeys which have fission-fusion social structure (FFSS). In this paper, a modified spider monkey optimization algorithm is proposed. The self-adaptive inertia weight is introduced in the local leader phase to enhance the self-learning ability of the spider monkey. According to the function value of an individual, the distance from the optimal value is determined, so the inertia weight related the individual function value is added to strength the global search ability or local search ability. The proposed algorithm is tested on 20 benchmark problems and compared with the original SMO and the hybrid algorithm SMOGA and GASMO. The numerical results show that the proposed algorithm has a certain degree of improvement in convergence accuracy and convergence speed. The performance of the proposed algorithm is also inspected by two classical engineering design problems.

Improved whale optimization algorithm based on variable spiral position update strategy and adaptive inertia weight

In this paper, a whale optimization algorithm based on adaptive inertia weight and variable spiral position updating strategy is proposed. The improved algorithm is used to solve the problem that the whale optimization algorithm is more dependent on the randomness of the parameters, so that the algorithm’s convergence accuracy and convergence speed are insufficient. The adaptive inertia weight, which varies with the fitness of individual whales, is used to balance the algorithm’s global search ability and local exploitation ability. The variable spiral position update strategy based on the collaborative convergence mechanism is used to dynamically adjust the search range and search accuracy of the algorithm. The effective combination of the two can make the improved whale optimization algorithm converge to the optimal solution faster. It had been used 18 international standard test functions, including unimodal function, multimodal function, and fixed-dimensional function to test the improved whale optimization algorithm in this paper. The test results show that the improved algorithm has faster convergence speed and higher algorithm accuracy than the original algorithm and several classic algorithms. The algorithm can quickly converge to near the optimal value in the early stage, and then effectively jump out of the local optimal through adaptive adjustment, and has a certain ability to solve large-scale optimization problems.

Improved Opposition-Based Particle Swarm Optimization Algorithm for Global Optimization

Particle Swarm Optimization (PSO) has been widely used to solve various types of optimization problems. An efficient algorithm must have symmetry of information between participating entities. Enhancing algorithm efficiency relative to the symmetric concept is a critical challenge in the field of information security. PSO also becomes trapped into local optima similarly to other nature-inspired algorithms. The literature depicts that in order to solve pre-mature convergence for PSO algorithms, researchers have adopted various parameters such as population initialization and inertia weight that can provide excellent results with respect to real world problems. This study proposed two newly improved variants of PSO termed Threefry with opposition-based PSO ranked inertia weight (ORIW-PSO-TF) and Philox with opposition-based PSO ranked inertia weight (ORIW-PSO-P) (ORIW-PSO-P). In the proposed variants, we incorporated three novel modifications: (1) pseudo-random sequence Threefry and Philox utilization for the initialization of population; (2) increased population diversity opposition-based learning is used; and (3) a novel introduction of opposition-based rank-based inertia weight to amplify the execution of standard PSO for the acceleration of the convergence speed. The proposed variants are examined on sixteen bench mark test functions and compared with conventional approaches. Similarly, statistical tests are also applied on the simulation results in order to obtain an accurate level of significance. Both proposed variants show highest performance on the stated benchmark functions over the standard approaches. In addition to this, the proposed variants ORIW-PSO-P and ORIW-PSO-P have been examined with respect to training of the artificial neural network (ANN). We have performed experiments using fifteen benchmark datasets obtained and applied from the repository of UCI. Simulation results have shown that the training of an ANN with ORIW-PSO-P and ORIW-PSO-P algorithms provides the best results than compared to traditional methodologies. All the observations from our simulations conclude that the proposed ASOA is superior to conventional optimizers. In addition, the results of our study predict how the proposed opposition-based method profoundly impacts diversity and convergence.

Research on Reactive Power Optimization of Power System Based on Improved Particle Swarm Algorith

Artificial intelligence algorithms are widely used to optimize problems in power systems, and reactive power optimization in power systems has achieved good results in particle swarm optimization, but there are also problems. This paper optimizes the particle swarm algorithm. The particle swarm algorithm is improved mainly by increasing the inertia weight and improving the convergence parameters. This algorithm overcomes the blindness of local optimization solution and particle swarm algorithm, and improves the calculation speed. At the same time, MATLAB is used to compile the calculation program, and the simulation results are used to verify the feasibility of the reactive power optimization algorithm used in the research of power system.

Cloud- and Fog-Integrated Smart Grid Model for Efficient Resource Utilisation

The smart grid (SG) is a contemporary electrical network that enhances the network’s performance, reliability, stability, and energy efficiency. The integration of cloud and fog computing with SG can increase its efficiency. The combination of SG with cloud computing enhances resource allocation. To minimise the burden on the Cloud and optimise resource allocation, the concept of fog computing integration with cloud computing is presented. Fog has three essential functionalities: location awareness, low latency, and mobility. We offer a cloud and fog-based architecture for information management in this study. By allocating virtual machines using a load-balancing mechanism, fog computing makes the system more efficient (VMs). We proposed a novel approach based on binary particle swarm optimisation with inertia weight adjusted using simulated annealing. The technique is named BPSOSA. Inertia weight is an important factor in BPSOSA which adjusts the size of the search space for finding the optimal solution. The BPSOSA technique is compared against the round robin, odds algorithm, and ant colony optimisation. In terms of response time, BPSOSA outperforms round robin, odds algorithm, and ant colony optimisation by 53.99 ms, 82.08 ms, and 81.58 ms, respectively. In terms of processing time, BPSOSA outperforms round robin, odds algorithm, and ant colony optimisation by 52.94 ms, 81.20 ms, and 80.56 ms, respectively. Compared to BPSOSA, ant colony optimisation has slightly better cost efficiency, however, the difference is insignificant.