James J. Q. Yu

Real-Coded Chemical Reaction Optimization

Optimization problems can generally be classified as continuous and discrete, based on the nature of the solution space. A recently developed chemical-reaction-inspired metaheuristic, called chemical reaction optimization (CRO), has been shown to perform well in many optimization problems in the discrete domain. This paper is dedicated to proposing a real-coded version of CRO, namely, RCCRO, to solve continuous optimization problems. We compare the performance of RCCRO with a large number of optimization techniques on a large set of standard continuous benchmark functions. We find that RCCRO outperforms all the others on the average. We also propose an adaptive scheme for RCCRO which can improve the performance effectively. This shows that CRO is suitable for solving problems in the continuous domain.

A social spider algorithm for global optimization

Graphical abstractDisplay Omitted HighlightsWe propose a new nature-inspired social-spider-based swarm intelligence algorithm.We introduce a new social animal foraging model into meta-heuristic design.We introduce the design of information loss to handle pre-mature convergence.We perform a series of benchmark simulations to demonstrate the performance.We investigate the impact of control parameters on optimization results. The growing complexity of real-world problems has motivated computer scientists to search for efficient problem-solving methods. Metaheuristics based on evolutionary computation and swarm intelligence are outstanding examples of nature-inspired solution techniques. Inspired by the social spiders, we propose a novel social spider algorithm to solve global optimization problems. This algorithm is mainly based on the foraging strategy of social spiders, utilizing the vibrations on the spider web to determine the positions of preys. Different from the previously proposed swarm intelligence algorithms, we introduce a new social animal foraging strategy model to solve optimization problems. In addition, we perform preliminary parameter sensitivity analysis for our proposed algorithm, developing guidelines for choosing the parameter values. The social spider algorithm is evaluated by a series of widely used benchmark functions, and our proposed algorithm has superior performance compared with other state-of-the-art metaheuristics.

Evolutionary artificial neural network based on Chemical Reaction Optimization

Evolutionary algorithms (EAs) are very popular tools to design and evolve artificial neural networks (ANNs), especially to train them. These methods have advantages over the conventional backpropagation (BP) method because of their low computational requirement when searching in a large solution space. In this paper, we employ Chemical Reaction Optimization (CRO), a newly developed global optimization method, to replace BP in training neural networks. CRO is a population-based metaheuristics mimicking the transition of molecules and their interactions in a chemical reaction. Simulation results show that CRO outperforms many EA strategies commonly used to train neural networks.

A social spider algorithm for solving the non-convex economic load dispatch problem

Economic Load Dispatch (ELD) is one of the essential components in power system control and operation. Although conventional ELD formulation can be solved using mathematical programming techniques, modern power system introduces new models of the power units which are non-convex, non-differentiable, and sometimes non-continuous. In order to solve such non-convex ELD problems, in this paper we propose a new approach based on the Social Spider Algorithm (SSA). The classical SSA is modified and enhanced to adapt to the unique characteristics of ELD problems, e.g., valve-point effects, multi-fuel operations, prohibited operating zones, and line losses. To demonstrate the superiority of our proposed approach, five widely adopted test systems are employed and the simulation results are compared with the state-of-the-art algorithms. In addition, the parameter sensitivity is illustrated by a series of simulations. The simulation results show that SSA can solve ELD problems effectively and efficiently.

Power-Controlled Cognitive Radio Spectrum Allocation with Chemical Reaction Optimization

Cognitive radio is a promising technology for increasing the system capacity by using the radio spectrum more effectively. It has been widely studied recently and one important problem in this new paradigm is the allocation of radio spectrum to secondary users effectively in the presence of primary users. We call it the cognitive radio spectrum allocation problem (CRSAP) in this paper. In the conventional problem formulation, a secondary user can be either on or off and its interference range becomes maximum or zero, respectively. We first develop a solution to CRSAP based on the newly proposed chemical reaction-inspired metaheuristic called Chemical Reaction Optimization (CRO). We study different utility functions, accounting for utilization and fairness, with the consideration of the hardware constraint, and compare the performance of our proposed CRO-based algorithm with existing ones. Simulation results show that the CRO-based algorithm always outperforms the others dramatically. Next, by allowing adjustable transmission power, we propose power-controlled CRSAP (PC-CRSAP), a new formulation to the problem with the consideration of spatial diversity. We design a two-phase algorithm to solve PC-CRSAP, and again simulation results show excellent performance.

Chemical Reaction Optimization for the optimal power flow problem

This paper presents an implementation of the Chemical Reaction Optimization (CRO) algorithm to solve the optimal power flow (OPF) problem in power systems with the objective of minimizing generation costs. Multiple constraints, such as the balance of the power, bus voltage magnitude limits, transmission line flow limits, transformer tap settings, etc., are considered. We adapt the CRO framework to the OPF problem by redesigning the elementary reaction operators. We perform simulations on the standard IEEE-14, -30, and -57 bus benchmark systems. We compare the perform of CRO with other reported evolutionary algorithms in the IEEE-30 test case. Simulation results show that CRO can obtain a solution with the lowest cost, when compared with other algorithms. To be more complete, we also give the average result for the IEEE-30 case, and the best and average results for the IEEE-14 and -57 test cases. The results given in this paper suggest that CRO is a better alternative for solving the OPF problem, as well as its variants for the future smart grid.

Sensor deployment for air pollution monitoring using public transportation system

Air pollution monitoring is a very popular research topic and many monitoring systems have been developed. In this paper, we formulate the Bus Sensor Deployment Problem (BSDP) to select the bus routes on which sensors are deployed, and we use Chemical Reaction Optimization (CRO) to solve BSDP. CRO is a recently proposed metaheuristic designed to solve a wide range of optimization problems. Using the real world data, namely Hong Kong Island bus route data, we perform a series of simulations and the results show that CRO is capable of solving this optimization problem efficiently.

Intelligent Time-Adaptive Transient Stability Assessment System

Online identification of postcontingency transient stability is essential in power system control, as it facilitates the grid operator to decide and coordinate system failure correction control actions. Utilizing machine learning methods with synchrophasor measurements for transient stability assessment has received much attention recently with the gradual deployment of wide-area protection and control systems. In this paper, we develop a transient stability assessment system based on the long short-term memory network. By proposing a temporal self-adaptive scheme, our proposed system aims to balance the trade-off between assessment accuracy and response time, both of which may be crucial in real-world scenarios. Compared with previous work, the most significant enhancement is that our system learns from the temporal data dependencies of the input data, which contributes to better assessment accuracy. In addition, the model structure of our system is relatively less complex, speeding up the model training process. Case studies on three power systems demonstrate the efficacy of the proposed transient stability as sessment system.

Real-coded chemical reaction optimization with different perturbation functions

Chemical Reaction Optimization (CRO) is a powerful metaheuristic which mimics the interactions of molecules in chemical reactions to search for the global optimum. The perturbation function greatly influences the performance of CRO on solving different continuous problems. In this paper, we study four different probability distributions, namely, the Gaussian distribution, the Cauchy distribution, the exponential distribution, and a modified Rayleigh distribution, for the perturbation function of CRO. Different distributions have different impacts on the solutions. The distributions are tested by a set of wellknown benchmark functions and simulation results show that problems with different characteristics have different preference on the distribution function. Our study gives guidelines to design CRO for different types of optimization problems.

Base station switching problem for green cellular networks with Social Spider Algorithm

With the recent explosion in mobile data, the energy consumption and carbon footprint of the mobile communications industry is rapidly increasing. It is critical to develop more energy-efficient systems in order to reduce the potential harmful effects to the environment. One potential strategy is to switch off some of the under-utilized base stations during off-peak hours. In this paper, we propose a binary Social Spider Algorithm to give guidelines for selecting base stations to switch off. In our implementation, we use a penalty function to formulate the problem and manage to bypass the large number of constraints in the original optimization problem. We adopt several randomly generated cellular networks for simulation and the results indicate that our algorithm can generate superior performance.