A Survey of Swarm Intelligence Techniques in VLSI Routing Problems <s> 3) HYBRID SI ALGORITHMS <s> This paper presents a novel two-stage hybrid swarm intelligence optimization algorithm called GA---PSO---ACO algorithm that combines the evolution ideas of the genetic algorithms, particle swarm optimization and ant colony optimization based on the compensation for solving the traveling salesman problem. In the proposed hybrid algorithm, the whole process is divided into two stages. In the first stage, we make use of the randomicity, rapidity and wholeness of the genetic algorithms and particle swarm optimization to obtain a series of sub-optimal solutions (rough searching) to adjust the initial allocation of pheromone in the ACO. In the second stage, we make use of these advantages of the parallel, positive feedback and high accuracy of solution to implement solving of whole problem (detailed searching). To verify the effectiveness and efficiency of the proposed hybrid algorithm, various scale benchmark problems from TSPLIB are tested to demonstrate the potential of the proposed two-stage hybrid swarm intelligence optimization algorithm. The simulation examples demonstrate that the GA---PSO---ACO algorithm can greatly improve the computing efficiency for solving the TSP and outperforms the Tabu Search, genetic algorithms, particle swarm optimization, ant colony optimization, PS---ACO and other methods in solution quality. And the experimental results demonstrate that convergence is faster and better when the scale of TSP increases. <s> BIB001 </s> A Survey of Swarm Intelligence Techniques in VLSI Routing Problems <s> 3) HYBRID SI ALGORITHMS <s> In this paper, a novel hybrid algorithm based on artificial bee colony (ABC) algorithm and differential evolution (DE) algorithm called ABC-DE is proposed to inherit their advantages and overcome their drawbacks. In ABC-DE algorithm, employed bees employ the mutation and crossover strategies of DE to enforce their exploration ability while onlooker bees keep their original updating strategy to retain the exploitation ability. Classical test functions have been provided to testify the ABC-DE algorithm. Furthermore, ABC-DE is applied to different types of time-modulated arrays (TMAs) pattern synthesis problems, including the syntheses of low equal-ripple sidelobe level (SLL) pattern, deep null level pattern, multiple-beams patterns and satellite footprint pattern. Experiment results reveal that ABC-DE has a promising performance in the convergence rate and the exploration ability when compared with other improved evolutionary algorithms, which indicates that the proposed algorithm can be an effective approach for TMAs synthesis problems. <s> BIB002 </s> A Survey of Swarm Intelligence Techniques in VLSI Routing Problems <s> 3) HYBRID SI ALGORITHMS <s> In this paper, a hybrid configuration of ant colony optimization (ACO) with artificial bee colony (ABC) algorithm called hybrid ACO–ABC algorithm is presented for optimal location and sizing of distributed energy resources (DERs) (i.e., gas turbine, fuel cell, and wind energy) on distribution systems. The proposed algorithm is a combined strategy based on the discrete (location optimization) and continuous (size optimization) structures to achieve advantages of the global and local search ability of ABC and ACO algorithms, respectively. Also, in the proposed algorithm, a multi-objective ABC is used to produce a set of non-dominated solutions which store in the external archive. The objectives consist of minimizing power losses, total emissions produced by substation and resources, total electrical energy cost, and improving the voltage stability. In order to investigate the impact of the uncertainty in the output of the wind energy and load demands, a probabilistic load flow is necessary. In this study, an efficient point estimate method (PEM) is employed to solve the optimization problem in a stochastic environment. The proposed algorithm is tested on the IEEE 33- and 69-bus distribution systems. The results demonstrate the potential and effectiveness of the proposed algorithm in comparison with those of other evolutionary optimization methods. <s> BIB003 </s> A Survey of Swarm Intelligence Techniques in VLSI Routing Problems <s> 3) HYBRID SI ALGORITHMS <s> Global optimization is challenging to solve due to its nonlinearity and multimodality. Traditional algorithms such as the gradient-based methods often struggle to deal with such problems and one of the current trends is to use metaheuristic algorithms. In this paper, a novel hybrid population-based global optimization algorithm, called hybrid firefly algorithm (HFA), is proposed by combining the advantages of both the firefly algorithm (FA) and differential evolution (DE). FA and DE are executed in parallel to promote information sharing among the population and thus enhance searching efficiency. In order to evaluate the performance and efficiency of the proposed algorithm, a diverse set of selected benchmark functions are employed and these functions fall into two groups: unimodal and multimodal. The experimental results show better performance of the proposed algorithm compared to the original version of the firefly algorithm (FA), differential evolution (DE) and particle swarm optimization (PSO) in the sense of avoiding local minima and increasing the convergence rate. <s> BIB004 </s> A Survey of Swarm Intelligence Techniques in VLSI Routing Problems <s> 3) HYBRID SI ALGORITHMS <s> Abstract Ant Colony Optimization (ACO) and Bee Colony Optimization (BCO) are famous meta-heuristic search algorithms used in solving numerous combinatorial optimization problems. Feature Selection (FS) helps to speed up the process of classification by extracting the relevant and useful information from the dataset. FS is seen as an optimization problem because selecting the appropriate feature subset is very important. This paper proposes a novel Swarm based hybrid algorithm AC-ABC Hybrid, which combines the characteristics of Ant Colony and Artificial Bee Colony (ABC) algorithms to optimize feature selection. By hybridizing, we try to eliminate stagnation behavior of the ants and time consuming global search for initial solutions by the employed bees. In the proposed algorithm, Ants use exploitation by the Bees to determine the best Ant and best feature subset; Bees adapt the feature subsets generated by the Ants as their food sources. Thirteen UCI (University of California, Irvine) benchmark datasets have been used for the evaluation of the proposed algorithm. Experimental results show the promising behavior of the proposed method in increasing the classification accuracies and optimal selection of features. <s> BIB005 </s> A Survey of Swarm Intelligence Techniques in VLSI Routing Problems <s> 3) HYBRID SI ALGORITHMS <s> Meta-heuristic search techniques, mainly Genetic Algorithm (GA), have been widely applied for automated test data generation according to a structural test adequacy criterion . However, it remains a challenging task for more robust adequacy criterion such as data-flow coverage of a program. Now, focus is on the use of other highly-adaptive meta-heuristic search techniques such as Particle Swarm Optimization (PSO) and Differential Evolution (DE). In this paper, a hybrid (adaptive PSO and DE) algorithm is proposed to generate test data for data-flow dependencies of a program with a neighbourhood search strategy to improve the search capability of the hybrid algorithm. The fitness function is based on the concepts of dominance relations and branch distance. The measures considered are mean number of generations and mean percentage coverage. The performance of the hybrid algorithm is compared with that of DE, PSO, GA, and random search. Over several experiments on a set of benchmark programs, it is shown that the hybrid algorithm performed significantly better than DE, PSO, GA and random search in data-flow test data generation with respect to the measures collected. <s> BIB006 </s> A Survey of Swarm Intelligence Techniques in VLSI Routing Problems <s> 3) HYBRID SI ALGORITHMS <s> With the emergence of automated environments, energy demand by consumers is increasing rapidly. More than 80% of total electricity is being consumed in the residential sector. This brings a challenging task of maintaining the balance between demand and generation of electric power. In order to meet such challenges, a traditional grid is renovated by integrating two-way communication between the consumer and generation unit. To reduce electricity cost and peak load demand, demand side management (DSM) is modeled as an optimization problem, and the solution is obtained by applying meta-heuristic techniques with different pricing schemes. In this paper, an optimization technique, the hybrid gray wolf differential evolution (HGWDE), is proposed by merging enhanced differential evolution (EDE) and gray wolf optimization (GWO) scheme using real-time pricing (RTP) and critical peak pricing (CPP). Load shifting is performed from on-peak hours to off-peak hours depending on the electricity cost defined by the utility. However, there is a trade-off between user comfort and cost. To validate the performance of the proposed algorithm, simulations have been carried out in MATLAB. Results illustrate that using RTP, the peak to average ratio (PAR) is reduced to 53.02%, 29.02% and 26.55%, while the electricity bill is reduced to 12.81%, 12.012% and 12.95%, respectively, for the 15-, 30- and 60-min operational time interval (OTI). On the other hand, the PAR and electricity bill are reduced to 47.27%, 22.91%, 22% and 13.04%, 12%, 11.11% using the CPP tariff. <s> BIB007 </s> A Survey of Swarm Intelligence Techniques in VLSI Routing Problems <s> 3) HYBRID SI ALGORITHMS <s> Expanding network lifespan is the main target during the design of a wireless sensor network. Clustering the sensor nodes is an efficient topology to accomplish this objective. In this work, we offer a reactive hybrid protocol to enhance network lifetime using the hybridization of ant colony optimization (ACO) along with particle swarm optimization (PSO) algorithm. In order to improve the energy efficiency, the anticipated RAP algorithm uses a reactive data transmission strategy which is incorporated into the hybridization of ACO and PSO algorithm. In the beginning, the clusters are organized depending on the residual energy and then the proposed RAP algorithm will be executed to improvise the inter-cluster data aggregation and reduces the data transmission. The experimental outcomes demonstrate the proposed RAP algorithm performs well against other near conventions in different situations. <s> BIB008 </s> A Survey of Swarm Intelligence Techniques in VLSI Routing Problems <s> 3) HYBRID SI ALGORITHMS <s> This paper introduces a CEED model for smart grid system and solves it by hybridizing the renowned optimization algorithms such as Particle Swarm Optimization (PSO) and Differential Evolution (DE). The hybridization of these two renowned algorithms is accomplished by using the solution updating process of both the algorithms and combining them with random searching procedure. The CEED model is subjected to minimize its cost so that adequate trade – off between the economic and emission costs can be maintained in minimizing them. The proposed hybrid algorithm is experimented on three different bus systems and its performance is compared against individual PSO and DE and a recently framed hybridization of PSO and DE. The comparison results show the superiority of the proposed hybrid heuristic search algorithm in terms of solution quality and the computational efficiency. <s> BIB009 </s> A Survey of Swarm Intelligence Techniques in VLSI Routing Problems <s> 3) HYBRID SI ALGORITHMS <s> In cloud computing, varied demands are placed on the constantly changing resources. The task scheduling place very vital role in cloud computing environments, this scheduling process needs to schedule the tasks to virtual machine while reducing the makespan and cost. The task scheduling problem comes under NP hard category. Efficient scheduling method makes cloud computing services better and faster. In general, optimization algorithms are used to solve the scheduling issues in cloud. So, in this paper we combined two optimization algorithms namely called as Cuckoo Search (CS) and Particle Swarm Optimization (PSO).The new proposed hybrid algorithm is called as, CS and particle swarm optimization (CPSO). Our main purpose of the proposed paper is to reduce the makespan, cost and deadline violation rate. The performance of the proposed CPSO algorithm is evaluated using cloudsim toolkit. From the simulation results our proposed works minimize the makespan, cost, deadline violation rate, when compared to PBACO, ACO, MIN–MIN, and FCFS. <s> BIB010
Each SI technique has its own unique properties and will have different effects in different application scenarios. In recent years, in order to improve the performance of various SI techniques, and further improve the quality of the solution, various hybrid SI techniques: PSO-ACO BIB001  BIB008 , ACO-ABC BIB003 , BIB005 , DE-ABC BIB002 ,  , FA-DE BIB004 , PSO-DE BIB006 , BIB009 and other hybrid SI algorithms BIB007 , BIB010 are proposed and successfully applied in various fields. Reference BIB001 proposes a two-stage hybrid swarm intelligence optimization algorithm, using the randomicity, rapidity and wholeness of PSO and GA for rough searching; and for detailed searching, they made use of the parallel, positive feedback and high accuracy of solution of ACO. Kefayat et al. BIB003 took the advantages of the global search ability of ABC and the local search ability of ACO to make up for the shortcomings of ACO that are easy to fall into local optimum. In the hybrid DE-ABC algorithm proposed by Yang et al. BIB002 , employed bees employ the mutation and crossover strategies of DE to enforce their exploration ability while onlooker bees keep their original updating strategy to retain the exploitation ability. This algorithm improves the convergence speed and searching ability of ABC. Using a single SI technique is easily affected by the initial population and parameter settings, while mixing different SI technologies, or communicating information through the co-evolution of different types of populations, or learning some mechanisms from other SI techniques can greatly improve the performance of the algorithm and avoid falling into local extremes as much as possible. The above successful cases show that the hybrid SI algorithms have opportunity to be better applied to VLSI routing problems than the single SI technique.