Artificial Physics Optimization Algorithm Research Articles

An Improved Multi-Objective Artificial Physics Optimization Algorithm Based on Multi-Strategy Fusion

The elements of the population of the artificial physics optimization (APO) algorithm are assigned mass, velocity, and displacement attributes. It is a new type of heuristic algorithm, but it still has defects such as low efficiency of non-dominated solution selection and unbalanced search capacity of global and local. This paper introduces the mechanism of improved fast non-dominant sorting and partition-guided individual evolution into the APO algorithm to overcome this imperfection. An improved multi-objective artificial physics optimization algorithm based on fast non-dominated sorting (IFNS-MOAPO) is proposed, which is the result of integrating numerous strategies into the APO algorithm. Firstly, an improved fast non-dominated sorting strategy is introduced. This strategy can increase the efficiency of selecting non-dominated solutions and decrease the running time of the algorithm. Secondly, the mechanism of individual evolution guided by partition is proposed. For individuals in infeasible and feasible domains, different mass functions and virtual force calculation rules are adopted to update the algorithm iteratively to boost the convergence performance. To verify the comprehensive performance of the IFNS-MOAPO algorithm, seven benchmark test problems are selected for simulation experiments and compared with five algorithms in terms of runtime duration, Pareto front plots, and metric values. The results show that the IFNS-MOAPO algorithm has good distribution and can converge to the true Pareto front quickly. It is a useful tool for solving constrained multi-objective optimization problems (CMOPs).

Research on a new optimization algorithm simulating multi- states of matter inspired by finite element analysis approach

A new optimization algorithm is proposed, since a huge problem that many algorithms faced was not being able to effectively balance the global and local search ability. Matter exists in three states: solid, liquid, and gas, which presents different motion characteristics. Inspired by multi- states of matter, individuals of optimization algorithm have different motion characteristics of matter, which could present different search ability. The Finite Element Analysis (FEA) approach can simulate multi- states of matter, which can be adopted to effectively balance the global search ability and local search ability in new optimization algorithm. The new algorithm is creative application of Finite Element Analysis at optimization algorithm field. Artificial Physics Optimization (APO) and Gravitational Search Algorithm (GSA) belongs to the algorithm types defined by force and mass. According to FEA approach, node displacement caused by force and stiffness could be equivalent to motion caused by force and mass of APO and GSA. In the new algorithm framework, stiffness replaces mass of APO and GSA algorithm. This paper performs research on two different algorithms based on APO and GSA respectively. The individuals of new optimization algorithm are divided into solid state, liquid state, and gas state. The effects of main parameters on the performance were studied through experiments of 6 static test functions. The performance is compared with PSO, basic APO, or GSA for four complex models which made up of solid individual, liquid individual, and gas individual in iterative process. The reasonable complex model can be confirmed experimentally. Based on the reasonable complex model, the article conducted complete experiments against Enhancing artificial bee colony algorithm with multi-elite guidance (MGABC), Artificial bee colony algorithm with an adaptive greedy position update strategy (AABC), Multi-strategy ensemble artificial bee colony (MEABC), Self-adaptive heterogeneous PSO (fk-PSO), and APO with 28 CEC2013 test problem. Experimental results show that the proposed method achieves a good performance in comparison to its counterparts as a consequence of its better exploration– exploitation balance. The algorithm supplies a new method to improve physics optimization algorithm.

An Improved Artificial Physics Optimization Algorithm Approach for Static Power System Security Analysis

The major functions involved in power system security analysis are security assessment and security enhancement. The contingency analysis plays a vital role in the security assessment which has been carried out based on the performance index. Security enhancement incorporates security constrained optimal power flow (SCOPF) which ensures that system is operating at normal state by taking preventive and control actions. An improved artificial physics optimization (APO) evolutionary algorithm is presented in this paper for solving SCOPF problem. The trial and error approach is used for selection of gravitational constant (G) in standard APO method. In order to circumvent this problem, a fuzzy approach is used to tune the gravitational constant (G). Simulations are done on IEEE 30-bus system, Indian 75-bus system, and IEEE 118-bus test system. To evaluate the robustness, results obtained using the proposed method are compared with results obtained using standard conventional algorithms, namely APO, Bat algorithm, and genetic algorithm. The obtained results indicate that the proposed fuzzy adaptive artificial physics optimization (FAAPO) method is efficient as well as robust for solving SCOPF problem to get near global optimization solution compared to standard APO algorithm and other heuristic methods reported in the literature.

Multi-objective optimization of spectrum detection in cognitive IoT using artificial physics

ABSTRACTIn cognitive Internet of Things (C-IoT), spectrum detection aims to find the available spectrum resources for cognitive sensor nodes. However, it always consumes more energy to get higher detection rate in spectrum detection, so energy consumption and detection rate are positively correlated in C-IoT. Different from the available algorithms, we model spectrum detection in C-IoT as a multi-objective optimization problem and aim to find the trade-off points of spectrum detection. An artificial physics optimization algorithm is proposed to solve spectrum detection problems in C-IoT. The simulation results show that the proposed algorithm can effectively reduce the energy consumption and keep a high detection rate.

Application of Improved APO Algorithm in Vulnerability Assessment and Reconstruction of Microgrid

Artificial Physics Optimization (APO) has good global search ability and can avoid the premature convergence phenomenon in PSO algorithm, which has good stability of fast convergence and robustness. On the basis of APO of the vector model, a reactive power optimization algorithm based on improved APO algorithm is proposed for the static structure and dynamic operation characteristics of microgrid. The simulation test is carried out through the IEEE 30-bus system and the result shows that the algorithm has better efficiency and accuracy compared with other optimization algorithms.

Dynamic Power System Security Analysis Using a Hybrid PSO-APO Algorithm

In this paper, a novel hybrid particle swarm optimization and artificial physics optimization (HPSO-APO) algorithm is proposed to solve the dynamic security constrained optimal power flow (DSCOPF) problem for enhancing system security. The dynamic security assessment deals with contingency analysis which is carried out using a performance index. DSCOPF recommends preventive control actions like generator rescheduling to alleviate an existing credible contingency in the system while ensuring minimal operating cost. The OPF problem is a highly nonlinear differential one and becomes more complex when considering the rotor dynamics of the system. The APO algorithm has the capability to reach a near global optimum value. However, it suffers from convergence problem. On the other hand, PSO exhibits premature convergence characteristics, but it may get trapped at a local optima value. The proposed HPSO-APO algorithm combines both individual algorithm strengths, to get balance between global and local search capability. The proposed method has been evaluated on a standard IEEE six-generator, 30-bus system and a New England ten-generator, 39-bus test system. The proposed HPSO-APO algorithm gives an efficient and robust optimal solution of DSCOPF problem compared to standard PSO and APO methods.

Multi-objective hybrid PSO-APO algorithm based security constrained optimal power flow with wind and thermal generators

In this paper, a new low level with teamwork heterogeneous hybrid particle swarm optimization and artificial physics optimization (HPSO-APO) algorithm is proposed to solve the multi-objective security constrained optimal power flow (MO-SCOPF) problem. Being engaged with the environmental and total production cost concerns, wind energy is highly penetrating to the main grid. The total production cost, active power losses and security index are considered as the objective functions. These are simultaneously optimized using the proposed algorithm for base case and contingency cases. Though PSO algorithm exhibits good convergence characteristic, fails to give near optimal solution. On the other hand, the APO algorithm shows the capability of improving diversity in search space and also to reach a near global optimum point, whereas, APO is prone to premature convergence. The proposed hybrid HPSO-APO algorithm combines both individual algorithm strengths, to get balance between global and local search capability. The APO algorithm is improving diversity in the search space of the PSO algorithm. The hybrid optimization algorithm is employed to alleviate the line overloads by generator rescheduling during contingencies. The standard IEEE 30-bus and Indian 75-bus practical test systems are considered to evaluate the robustness of the proposed method. The simulation results reveal that the proposed HPSO-APO method is more efficient and robust than the standard PSO and APO methods in terms of getting diverse Pareto optimal solutions. Hence, the proposed hybrid method can be used for the large interconnected power system to solve MO-SCOPF problem with integration of wind and thermal generators.

Security-constrained optimal power flow with wind and thermal power generators using fuzzy adaptive artificial physics optimization algorithm

In this paper, a new fuzzy adaptive artificial physics optimization (FAAPO) algorithm is used to solve security-constrained optimal power flow (SCOPF) problem with wind and thermal power generators. The stochastic nature of wind speed is modeled as a Weibull probability density function. The production cost is modeled with the overestimation and underestimation of available wind energy and included in the conventional SCOPF. Wind generation cost model comprises two components, viz. reserve capacity cost for wind power surplus and penalty cost for wind power shortage. The selection of optimal gravitational constant (G) is a tedious process in conventional artificial physics optimization (APO) method. To overcome this limitation, the gravitational constant (G) is fuzzified in this work. Therefore, based upon the requirement, the gravitational constant changes adaptively. Hence, production cost is reduced, settles at optimum point and takes less number of iterations. The proposed algorithm is tested on IEEE 30-bus system and Indian 75-bus practical system, including wind power in both the test systems. It is observed that FAAPO can outperform BAT algorithm and APO algorithm. Hence, the proposed algorithm can be used for integration of wind power with thermal power generators.

Different force laws driving artificial physics optimisation algorithm for constrained optimisation problem

Inspired by physical force, Artificial Physics Optimisation APO algorithm is a novel stochastic based on a physicomimetics framework. Driven by virtual force, a population of sample individuals searches for a global optimum in the problem space. The force law is a key problem associated with the performance of APO algorithm significantly. In the paper, an APO algorithm with the Feasibility and Dominance FAD method FAD-APO is employed to solve constrained optimisation problems. Three different force laws are constructed between the feasible individuals and infeasible individuals, which drive all individuals to search for a global optimum in the constrained problem space. Simulation results show that FAD3-APO algorithm may generally perform better than FAD1-APO and FAD2-APO; it is the most stable and effective among the three versions of FAD-APO algorithms. Meanwhile, a comparison with other population-based heuristics shows that the FAD-APO algorithm is competitive on some test function.

Research and Application of Transmission Line Differentiated Operation and Maintenance Scheduling Optimization Model

Aimed at resources shortage and extensive management of transmission line operation and maintenance, this paper proposes a differentiated operation and maintenance (DOM) method. This approach divides DOM strategy into basis strategy and criteria problem based strategy. An multi-objective DOM scheduling optimized model is presented, which contains three objective function including economy, safety and reliability. Based on the current electric power enterprise management requirements, personal safety factor is incorporated into safety objective function. Multi-objective Artificial Physics Optimization algorithm (MOAPO) based on feasible rules of law is used to solve the problem with an excellent diversity and high efficiency. The model is used to optimize yearly differentiated operation and maintenance scheduling in a certain area’s transmission system, the result verifies the feasibility of the model and method.

Vulnerability assessment and reconfiguration of microgrid through search vector artificial physics optimization algorithm

Taking the mathematical model and reliability parameters of the microgrid into account, this paper has proposed two vulnerability assessment indices (VAI) – weighted complex network structure parameters and comprehensive operational sensitivity to set up a vulnerability assessment system. The microgrid reconfiguration (MR) model is built with consideration of the grid-connected/island mode and the VAI. A novel artificial physics optimization algorithm with a searching vector (SVAPO) is presented and applied to minimize the system vulnerability. The technique has been successfully implemented on CERTS and 38-bus microgrids to demonstrate the performance and effectiveness of the proposed method. The results obtained are encouraging.

On Optimal Planning for DNA Nanomechanical Robots

In this paper, we consider the optimal reconfiguration planning problem of finding the least number of reconfiguration steps to transform between two configurations for chain-type modular robots. We propose an intelligent algorithm for solution of the problem. In particular, we use the set of parameterized k-covers problem and the approximate period problem to detect periodic regularities in genetic sequences of DNA nanomechanical robots. We try to use similar reconfiguration actions for similar parts of genetic sequences. We consider an artificial physics optimization algorithm. We use Runge Kutta neural networks for the prediction of virtual force law.

Spectrum allocation of cognitive radio network based on artificial physics optimization

To study the spectrum allocation problem based on graph coloring model in cognitive radio network, an algorithm to maximize total network revenue is proposed, which is based on artificial physics optimization because of its NP-based features. In artificial physics optimization algorithm, the solution of spectrum allocation problem is mapped into a particle with mass. It establishes the relation between particle mass and its fitness value, and defines the virtual force between the particles by the law of gravity so that the entire group can move to the better direction and achieve population optimization. The detailed spectrum allocation process is given and the particle position updating equation is improved because of its binary coding features. Simulation results show that the proposed algorithm can better maximize network revenue.

A multi-objective artificial physics optimization algorithm based on ranks of individuals

This paper proposes a multi-objective artificial physics optimization algorithm based on individuals' ranks. Using a Pareto sorting based technique and incorporating the concept of neighborhood crowding degree, evolutionary individuals in the search space are evaluated at first. Then each individual is assigned a unique serial number in terms of its performance, which affects the mass of the individual. Thereby, the population evolves towards the direction of the Pareto-optimal front. Synchronously, the presented approach has good diversity, such that the population is spread evenly on the Pareto front. Results of simulation on a number of difficult test problems show that the proposed algorithm, with less evolutionary generations, is able to find a better spread of solutions and better convergence near the true Pareto-optimal front compared to classical multi-objective evolutionary algorithms (NSGA, SPEA, MOPSO) and to simple multi-objective artificial physics optimization algorithm.

Swarm robots search based on artificial physics optimisation algorithm

Swarm robotics is an important research area of swarm intelligence. Swarm robots searching potential target provides a better way in victim search or rescue operations in some disaster scenarios. This paper uses artificial physics optimisation APO algorithm as the modelling tool for solving swarm robots searching problem. Firstly, viewed as an individual moving in a closed two-dimensional workspace, each robot is abstracted as one order inertial element. Further, the model of swarm robots search for target with global sense based on APO algorithm is given, in which the virtual force law among robots is constructed through referring the attraction-repulsion rule of APO. Simulation results under an ideal environment show that APO is a feasible and effective when applied to swarm robotic search for target with global sense.

The selection strategy of mass functions in artificial physics optimisation algorithm

Inspired by physicomimetics, artificial physics optimisation (APO) is a novel population-based stochastic algorithm. In APO framework, the mass of each individual corresponds to a user-defined function of the value of an objective to be optimised, which can supply some important information for searching global optima. There are many functions that can be used as mass function, and no doubt some will be better than others for specific optimisation problems or perhaps classes of problems. This paper proposes the basic requirement and design method of mass function, and classifies mass functions into four different types of curvilinear functions according to their curvilinear styles, such as linear function, convex function, and concave function, etc. Simulation results show the mass functions with concave curve may generally obtain the satisfied solution within the allowed iterations.

Artificial physics optimisation algorithm guided by diversity

In order to avoid the stagnation evolution of APO population, the thinking of dissipative structure theory and population diversity are combined in APO. Firstly, a chaos factor is introduced to judge whether the individuals doing dissipative movement or not, which is defined in a dissipation rule. However, the behaviour of an individual decided by the dissipation rule has blindness. Hence, population diversity is used to guide individual's movement. Then a diversity factor is introduced to judge whether population diversity is good or bad. If population diversity is worse than the diversity factor, individuals will do dissipative movement according to dissipation rule. The proposed algorithm is called APO algorithm guide by diversity APOD. Simulation results show APOD algorithm can improve the population diversity and global search capability of APO algorithm.

Mass functions design of artificial physics optimisation algorithm for constrained optimisation problem

Artificial physics optimisation APO is a novel population-based stochastic algorithm inspired by physicomimetics. APO with the feasibility and dominance method EAD-APO is employed to solve constrained optimisation problems. In EAD-APO, the mass of each feasible individual corresponds to a user-defined function of the value of an objective to be optimised, and the mass of each infeasible individual corresponds to a user-defined function of the constraint violation value, which can supply some important information for searching global optima. There are many functions can be used as mass function, and no doubt some will be better than others for specific optimisation problems or perhaps classes of problems. This paper proposes the basic regulation and design method of mass function, and classifies mass functions into three different types of curvilinear functions according to their curvilinear styles, such as linear function, convex function, and concave function. Simulation results show the mass functions with concave curve may generally obtain the satisfied solution within the allowed iterations.

Inversion algorithm based on the generalized objective functional for compressed sensing

Owing to providing a novel insight for signal and image processing, compressed sensing (CS) has attracted increasing attention. The accuracy of the reconstruction algorithms plays an important role in real applications of the CS theory. In this paper, a generalized reconstruction model that simultaneously considers the inaccuracies on the measurement matrix and the measurement data is proposed for CS reconstruction. A generalized objective functional, which integrates the advantages of the least squares (LSs) estimation and the combinational M-estimation, is proposed. An iterative scheme that integrates the merits of the homotopy method and the artificial physics optimization (APO) algorithm is developed for solving the proposed objective functional. Numerical simulations are implemented to evaluate the feasibility and effectiveness of the proposed algorithm. For the cases simulated in this paper, the reconstruction accuracy is improved, which indicates that the proposed algorithm is successful in solving CS inverse problems.

Convergence analysis and performance of the extended artificial physics optimization algorithm

This paper presents extended artificial physics optimization (EAPO), a population-based, stochastic, evolutionary algorithm (EA) for multidimensional search and optimization. EAPO extends the physicomimetics-based Artificial Physics Optimization (APO) algorithm by including each individual’s best fitness history. Including the history improves EAPO’s search capability compared to APO. EAPO and APO invoke a gravitational metaphor in which the force of gravity may be attractive or repulsive, the aggregate effect of which is to move individuals toward local and global optima. A proof of convergence is presented that reveals the conditions under which EAPO is guaranteed to converge. Discrete-time linear system theory is used to develop a second-order difference equation for an individual’s stochastic position vector as a function of time step. Stable solutions require eigenvalues inside the unit circle, leading to explicit convergence criteria relating the run parameters { m i , w, G}. EAPO is tested against several benchmark functions with excellent results. The algorithm converges more quickly than APO and with better diversity.