A. J. Umbarkar

Dual Population Genetic Algorithm (GA) versus OpenMP GA for Multimodal Function Optimization

algorithms (GAs) are useful for solving multimodal problems. It is quite difficult to search the search space of the multimodal problem with large dimensions. There is a challenge to use all the core of the system. The Dual Population GA (DPGA) attempts to explore and exploit search space on the multimodal problems. Parallel GAs (PGAs) are better option to optimize multimodal problems. OpenMP GA is parallel version of GA. The Dual Population GA (DPGA) uses an extra population called reserve population to provide additional diversity to the main population through crossbreeding. DPGA and PGA, both provide niching technique to find optimal solution. Paper presents the experimentation of DPGA, OpenMP GA and SGA. The experimentation results show that the performance of the OpenMP GA is remarkably superior to that of the SGA in terms of execution time and speed up. OpenMP GA gives optimum solution in comparison with OpenMP GA and SGA for same parameter settings. KeywordsAlgorithm (GA), Dual Population GA (DPGA), Serial DPGA, Open Multi Processing (OpenMP), Multimodal Function, Non-linear optimization problems.

Multithreaded Parallel Dual Population Genetic Algorithm (MPDPGA) for unconstrained function optimizations on multi-core system

Various problems viz. population diversity problem, premature convergence problem and curse of dimensionality problem, are associated with Genetic Algorithm (GA). Dual Population GA (DPGA) helps to provide additional population diversity to the main population by means of crossbreeding between the main population and reserve population. This helps to solve the problem of premature convergence and helps in early convergence of the algorithm. The binary encoded Multithreaded Parallel DPGA (MPDPGA) is proposed in this paper to solve the problems of population diversity and premature convergence. The experimental results show that, the performance (mean, standard deviation and standard error of mean), student t-test, mean function evaluation and success rate of MPDPGA is better than serial DPGA (SDPGA) and simple GA (SGA).

Solving 0/1 Knapsack Problem Using Hybrid TLBO-GA Algorithm

The 0/1 knapsack problem is attempted to solve using various soft computing methods till date. This paper proposes hybrid TLBO-GA algorithm which is hybrid of teaching learning-based optimization (TLBO) algorithm with genetic algorithm (GA). The 0/1 knapsack problem is a combinatorial optimization problem. The 0/1 knapsack problem aims to maximize the benefit of objects in a knapsack without exceeding its capacity as a constraint. In the literature, it is found that TLBO works for real-coded or real-valued problems. Hybrid TLBO-GA combines evolutionary process of TLBO and binary chromosome representation of GA for solving the knapsack problem (KP). Hybrid TLBO-GA combines advantages of both TLBO and GA. Results are taken on random as well as standard date sets using hybrid TLBO-GA for 0/1 knapsack problem. Hybrid TLBO-GA results are compared with the results obtained using simple genetic algorithm (SGA) on the same data sets. The results obtained using hybrid TLBO-GA are found satisfactory.

Serial DPGA vs. Parallel Multithreaded DPGA: Threading Aspects

The multiple main populations, reserve populations and subpopulations concepts of a Genetic Algorithms (GAs) offers the advantage of diversity. However, as the population evolves, the GA loses its diversity. As the population converges, it begins to lose its diversity and cannot avoid the local optima problem. This problem is known as Premature Convergence for Parallel GAs (PGA) too. The paper compares the Binary encoded Simple GA (SGA), Binary encoded Serial/ Sequential Dual Population Genetic Algorithm (SDPGA) and Binary encoded Multithreaded Parallel DPGA (MPDPGA) performances for function optimization on multicore system. The Dual Population Genetic Algorithm (DPGA) is an evolutionary algorithm that uses an extra population called the reserve population to provide additional diversity to the main population through crossbreeding. The experimental results on unimodal and multimodal classes of test problem shows the MPDPGA outperforms over SGA and SDPGA. The performance of MPDPGA with DPGA1 is better in terms of accuracy, number of generations and execution time on multicore system. The performance of MPDPGA with DPGA-ED1 is better for Rosenbrock and Schwefel whereas worse for Ackley and Griewangk.

Comparative study of diversity based parallel dual population genetic algorithm for unconstrained function optimisations

The genetic algorithms GAs metaheuristic deals with large scale combinatorial optimisation problems. It is biologically inspired by the method, based on the principle of survival of the fittest. In GAs, the concept of multiple populations offers an advantage of diversity. However, as the population evolves, the GA loses its diversity and sometimes it cannot avoid the local optima problem also known as premature convergence. The dual population genetic algorithm DPGA uses an extra population called the reserve population to provide additional diversity to the main population through crossbreeding. Crossbreeding solves the problem of premature convergence and helps to converge early. This paper is the empirical study of the Binary encoded parallel DPGA PDPGA. It is compared with metaheuristics given in literature based on reliability, efficacy and efficiency. The performance of PDPGA is competitive over other nature-inspired optimisation methods like genetic algorithm GA, particle swarm optimisation PSO, differential evolution DE, ANTS, bee colony, grenade explosion method GEM and bee colony optimisation BCO, but not better than artificial bee colony ABC and teaching-learning-based optimisation TLBO.

Diversity-Based Dual-Population Genetic Algorithm (DPGA): A Review

Maintaining population diversity is a challenge for the success of genetic algorithm. A numerous approaches have been proposed by researchers for adding diversity to the population. Dual-population genetic algorithm (DPGA) is one of them which is an effective optimization algorithm and provides diversity to the main population. Problems in GA such as premature convergence and population diversity is well addressed by DPGA. The aim of writing this review paper is to study how DPGA has been evolved. DPGA is inherently parallelizable, and hence, it can be port to parallel programming architecture for large-scale or large-dimension problems.