In order to mitigate the difficulty of balancing diversity and convergence in heuristic algorithm, this paper proposes an IF-memetic hybrid double particle swarm optimization (IFMHDPSO) based on intuitionistic fuzzy memetic framework and multi-attribute decision. There are two independent exploration and exploitation populations employing distributed strategies in which social reinforcement operator and collision rebound operator are proposed to improve diversity of algorithm and explore new areas in populations of exploration. Moreover, an intuitionistic fuzzy multi-attribute decision making is built up for comprehensively evaluating the solution space to get the potential global optimal solution area, which can guide the PSO (Particle Swarm Optimization) with Lamarckian mechanism to carry out the local search to achieve cooperation between populations under different strategies and reasonable allocation of computational resources. Compared with other 5 new evolutionary algorithms, IFMHDPSO is of better comprehensive optimization in 23 benchmark function test results.

To improve the distribution performance of multiobjective particle swarm optimization algorithm, an adaptive multiobjective particle swarm optimization algorithm, based on the decomposed archive, named AMOPSO-DA, is developed in this paper. First,an external archive update strategy, based on the spatial distribution information of optimal solutions, is designed to improve the searching ability of AMOPSO-DA. Second, an adaptive flying parameter adjustment strategy, based on the evolutionary direction information of each particle, is proposed to balance the exploration ability and the exploitation ability. Finally, this proposed AMOPSO-DA is applied to some multiobjective optimization problems. The experiment results demonstrate that AMOPSO-DA can obtain well-distributed optimal solutions.

In order to balance the exploration and exploitation of particle swarm optimization, this paper proposes a hybrid mean center opposition-based learning particle swarm optimization. The algorithm performs greedy selection on the mean center of all particles and some high-quality particles respectively, and the obtained hybrid mean center will search the region in detail where the particles are located. At the same time, the hybrid mean center is using opposition-based learning, so that the particles can explore more new regions. The proposed algorithm are compared with the latest improved particle swarm optimization, artificial bee colony algorithm and difference algorithm in various test function sets, and the results verify the effectiveness of the hybrid mean center opposition-based learning and the overall optimization performance of the algorithm is stronger.

To determine the population size of multi-objective particle swarm optimization algorithm (MOPSO), an improved MOPSO, based on the convergence speed and diversity, named CD-MOPSO, is proposed. Firstly, the fitness function of population size, which is developed by the convergence speed and diversity during the evolutionary process, is used to describe the relationship between the population size and the performance of MOPSO. Secondly, according to the fitness function, an adaptive adjustment method is designed to update the population size of MOPSO dynamically. Finally, the proposed CD-MOPSO is tested on the ZDT benchmark optimization problems and applied to a real optimization problem of urban pipe networks. The experimental results show that the proposed CD-MOPSO can adjust the population size automatically according to the problem, compared with the performance of NSGA, MOPSO, SPEA2 and EMDS-MOPSO, CD-MOPSO has faster convergence speed with better optimization results.

A new quantum chaos particle swarm optimization (QCPSO) was proposed to accurately estimate the uncertain parameters of the fractional order hyper chaotic system. The QCPSO algorithm was realized by analyzing the mechanism of quantum behaved particle swarm optimization (QPSO) and combining the correlation between quantum entanglement and chaotic system. Firstly, the center of potential well was replaced by a fixed point of chaotic attractor. The particles which outside the attractor were gradually converged to the attractor, and the particles which inside the attractor were quickly diffused. Secondly, in order to guarantee the diversity of the initial value of the chaotic particles, the particle update mechanism based on random mapping was proposed. Finally, a scale adaptive strategy was proposed to solve the problem of search stagnation of the algorithm. The parameters of fractional order hyper chaotic system were estimated by the QCPSO algorithm, and the results showed that the QCPSO algorithm has faster convergence speed and higher accuracy than improved differential evolution algorithm, adaptive artificial bee colony algorithm and improved QPSO algorithm.

In order to improve the global and local fine search capabilities of the particle swarm algorithm and accelerate the convergence speed, hybrid particle swarm optimization algorithm based on intuitive fuzzy entropy is proposed. The algorithm constructs an adaptive function of intuitive fuzzy entropy by using the information of the historical optimal solution of particles, and uses the entropy value as a disturbance factor to dynamically adjust the inertia weight. At the same time, it establishes an adaptive global optimal particle learning strategy to train the disturbed particles, chooses learning objects based on maintaining the diversity of propagation, enables the particles to explore more new areas, and realizes the cooperation and parallel evolution among populations. Through simulation experiments, the algorithm is compared with two derivation algorithms and other improved particle swarm optimization algorithms on 11 test functions. The results show that the algorithm performs better in solving accuracy, convergence speed and optimization efficiency.

In the actual production life conditions, a large number of multiple choices can be converted into a multiple-choice knapsack problem (MCKP), but MCKP is a classic NP-hard problem. Therefore, for very large scale MCKP, it is often only possible to use the particle swarm algorithm, wolf pack algorithm, fish swarm algorithm and so on to solve the problem. For swarm intelligence algorithms, efficient and fast greedy algorithms play a key role in the generation of initial solutions. Based on the convex Pareto algorithm (CPA), an improved Pareto algorithm (IPA) that can quickly get the linear programming dominated set is proposed. IPA firstly selects the minimum weight item of each set, then computes the value density of all items, and finally chooses the greedy choice of the item according to the value density from high to low. When the value of the greedy option is greater than the original selection of the item set, then IPA is iterated. The simulation results show that compared with CPA, the speed of IPA is increased by 98.86%. The PSO-IPA solution accuracy is increased by an average of 28.92%.

Multipath signals can be used to realize localization since they are abundant and contain geometry information of indoor environments. Based on this, this paper proposes a multipath-assisted target localization algorithm. Firstly, the fitness function about the target and scatterer locations is constructed with Time of Flight (TOF) differences. Then, the locations of the target and scatterers are searched jointly by Particle Swarm Optimization (PSO) and Angle of Arrivals (AOAs) that determines searching ranges. Secondly, the estimated locations of scatterers and TOF differences are used to estimate the target location. Finally, all target locations are clustered by using Affinity Propagation Clustering (APC), and a clustering criterion is proposed to eliminate big localization errors. The simulation results show that the proposed algorithm can achieve high localization accuracy with a single base station.

Aiming at the problem that whale optimization algorithm is easy to fall into local extreme value and slow convergence speed,this paper proposes a whale optimization algorithm based on adaptive weight and simulated annealing.The improved convergence weight strategy is used to adjust the convergence speed of the algorithm,and the global optimization ability of the whale optimization algorithm is enhanced by simulated annealing.In the simulation experiment,18 test functions were calculated and the genetic algorithm,the particle swarm optimization algorithm and the standard whale algorithm were compared and statistically analyzed.At the same time,the influence of the adaptive weight and simulated annealing on the whale optimization is compared.The results show that the improved algorithm has a significant improvement in the calculation of the extremum of the test function,and the effectiveness of the improved algorithm is verified.