S. G. Ponnambalam

A hybrid cuckoo search and genetic algorithm for reliability-redundancy allocation problems

Solving reliability and redundancy allocation problems via meta-heuristic algorithms has attracted increasing attention in recent years. In this study, a recently developed meta-heuristic optimization algorithm cuckoo search (CS) is hybridized with well-known genetic algorithm (GA) called CS-GA is proposed to solve the reliability and redundancy allocation problem. By embedding the genetic operators in standard CS, the balance between the exploration and exploitation ability further improved and more search space are observed during the algorithms performance. The computational results carried out on four classical reliability-redundancy allocation problems taken from the literature confirm the validity of the proposed algorithm. Experimental results are presented and compared with the best known solutions. The comparison results with other evolutionary optimization methods demonstrate that the proposed CS-GA algorithm proves to be extremely effective and efficient at locating optimal solutions.

Robotic U-shaped assembly line balancing using particle swarm optimization

Automation in an assembly line can be achieved using robots. In robotic U-shaped assembly line balancing (RUALB), robots are assigned to workstations to perform the assembly tasks on a U-shaped assembly line. The robots are expected to perform multiple tasks, because of their capabilities. U-shaped assembly line problems are derived from traditional assembly line problems and are relatively new. Tasks are assigned to the workstations when either all of their predecessors or all of their successors have already been assigned to workstations. The objective function considered in this article is to maximize the cycle time of the assembly line, which in turn helps to maximize the production rate of the assembly line. RUALB aims at the optimal assignment of tasks to the workstations and selection of the best fit robot to the workstations in a manner such that the cycle time is minimized. To solve this problem, a particle swarm optimization algorithm embedded with a heuristic allocation (consecutive) procedure is proposed. The consecutive heuristic is used to allocate the tasks to the workstation and to assign a best fit robot to that workstation. The proposed algorithm is evaluated using a wide variety of data sets. The results indicate that robotic U-shaped assembly lines perform better than robotic straight assembly lines in terms of cycle time.

Bio-inspired search algorithms to solve robotic assembly line balancing problems

AbstractnRobots are employed in assembly lines to increase the productivity. The objective of robotic assembly line balancing (rALB) problem is to balance the assembly line, by allocating equal amount of tasks to the workstations on the line while assigning the most efficient robot to perform the assembly task at the workstation. In this paper, bio-inspired search algorithms, viz. particle swarm optimization (PSO) algorithm and a hybrid cuckoo search and particle swarm optimization (CS-PSO), are proposed to balance the robotic assembly line with the objective of minimizing the cycle time. The performance of the proposed PSO and hybrid CS-PSO is evaluated using the 32 benchmark problems available in the literature. The simulation results show that both PSO and hybrid CS-PSO are capable of providing solutions within the upper bound obtained by hybrid GA, the only metaheuristic reported so far for rALB in the literature and comparable to the solutions obtained by IBM CPLEX Optimization solver. It is also observed that hybrid CS-PSO is performing better than PSO in terms of cycle time.

An effective hybrid cuckoo search and genetic algorithm for constrained engineering design optimization

This article presents an effective hybrid cuckoo search and genetic algorithm (HCSGA) for solving engineering design optimization problems involving problem-specific constraints and mixed variables such as integer, discrete and continuous variables. The proposed algorithm, HCSGA, is first applied to 13 standard benchmark constrained optimization functions and subsequently used to solve three well-known design problems reported in the literature. The numerical results obtained by HCSGA show competitive performance with respect to recent algorithms for constrained design optimization problems.

Tabu Search and Simulated Annealing Algorithms for Scheduling in Flow Shops with Lot Streaming

Abstract This paper addresses the problem of making sequencing and scheduling decisions for n Jobs in m-machine flow shops with a lot sizing constraint. Lot streaming (lot sizing) is the process of creating sublots to move the completed portion of a production sublot to downstream machines. The planning decisions become more complex when lot streaming is allowed. There is scope for efficient metaheuristics for scheduling problems in an m machine flow shop with lot streaming. In recent years, much attention has been given to heuristics and search techniques. This paper proposes two metaheuristics, namely a simulated annealing algorithm (SA) and a tabu search algorithm (TABU), to evolve the optimal sequence for makespan and total flow time criteria in an m-machine flow shop with lot streaming. The algorithms are evaluated by means of comparison with Bakers algorithm for 2m/c cases and the makespan criterion, which proves the capability of both of them.

Multi-objective genetic algorithm as channel selection method for P300 and motor imagery data set

As different region of the brain is associated with different mental activity, channel selection is commonly used to enhance the performance of multi-electrode electroencephalography (EEG) system by removing task-irrelevant and redundant channels. Various channel selection methods are successfully implemented in Brain-Computer Interface (BCI) system that uses one type of brain activity by earlier researchers. Upon realizing the limitation of conventional BCI systems, there has been increasing number of hybrid BCI systems. These hybrid systems use combinations of two brain activity patterns to enhance the functionality of a system. In this paper, three multi-objective genetic algorithms (GAs) are proposed to optimize the number of channels selected and system accuracy. The objective of this research is to investigate the optimal tradeoff between the classification accuracy of a BCI system and the number of selected channels. This tradeoff is important because different BCI applications have different priorities; some implementations prefer minimum number of channels while others favor the classification accuracy. The second objective of this research is to investigate the effectiveness of the GAs adopted as a channel selection method for BCI systems based on different brain activity. Three BCI Competition data sets are used to evaluate the performance of the proposed GAs. Non-parametric Friedman test (p-value=0.635) is also conducted and the result reveals that the significant reduction in number of channels does not have significant impact in the classification accuracy on the evaluation data. This confirms the validity of genetic algorithms as a channel selection method for both P300 and motor imagery data.

Modeling and simulation of Electrostatic Adhesion for Wall Climbing Robot

Electrostatic Adhesion (ESA) was chosen as the surface attachment mechanism for Wall Climbing Robot (WCR) for its qualitative advantages. ESA model was developed to account for both parallel-plane and fringing static fields. Such Electrostatic Pad model yields equations governing the ESA force and the geometrical conditions for maximum ESA pressure per pad area. This paper shows that the parameters due to applied voltage, and also geometrical properties, material properties and compliance to wall surface of the Electrostatic Pad do affect the ESA force. The simulation also shows that fringing fields may account up to above 10% of total generated ESA force.

A hybrid cuckoo search-genetic algorithm for hole-making sequence optimization

Biologically-inspired algorithms are stochastic search methods that emulate the behavior of natural biological evolution to produce better solutions and have been widely used to solve engineering optimization problems. In this paper, a new hybrid algorithm is proposed based on the breeding behavior of cuckoos and evolutionary strategies of genetic algorithm by combining the advantages of genetic algorithm into the cuckoo search algorithm. The proposed hybrid cuckoo search-genetic algorithm (CSGA) is used for the optimization of hole-making operations in which a hole may require various tools to machine its final size. The main objective considered here is to minimize the total non-cutting time of the machining process, including the tool positioning time and the tool switching time. The performance of CSGA is verified through solving a set of benchmark problems taken from the literature. The amount of improvement obtained for different problem sizes are reported and compared with those by ant colony optimization, particle swarm optimization, immune based algorithm and cuckoo search algorithm. The results of the tests show that CSGA is superior to the compared algorithms.

Differential evolution algorithm with local search for capacitated vehicle routing problem

This paper presents an improved differential evolution algorithm with local search DELS for solving the capacitated vehicle routing problem CVRP. The CVRP is a classical vehicle routing problem with additional constraint where the capacity of the vehicle travelling on a specific route cannot exceed the maximum vehicle capacity. Local search procedures help to explore new search areas and refine the solutions found. The proposed algorithm is tested on CVRP instances described by Augerat et al. and Christofides and Eilon. The proposed DELS approach generate quality solutions for the benchmark problems tested and are comparable to the algorithms reported in the literature.

Application of a hybridized cuckoo search-genetic algorithm to path optimization for PCB holes drilling process

The drilling path optimization problem is a NP-hard combinatorial optimization problem. Due to complexity and exponential growth of solution space with respect to the problem size, drilling path optimization problem attracts a great interest among the academicians. In this paper, a hybrid algorithm cuckoo search with genetic algorithm (hybrid-CSGA) is applied to solve the path optimization problem for printed circuit board (PCB) holes drilling process. It is shown that hybrid-CSGA reaches the near-optimal solution much earlier than the CS and GA approach for small and large size problem instances. The computational experience conducted in this research indicates that the proposed method is robust, efficient, capable to find the best path for the PCB holes drilling path optimization problem.