An improved multiobjective memetic algorithm for permutation flow shop scheduling

In this paper we address scheduling of the permutation flow shop with minimization of makespan and total flow time as the objectives. We propose a memetic algorithm (MA) to search for the set of nondominated solutions (the Pareto optimal solutions). The proposed MA adopts the permutation-based encoding and the fitness assignment mechanism of NSGAII. The main feature is the introduction of an NEH-based neighborhood function into the local search procedure. We also adjust the size of the neighborhood dynamically during the execution of the MA to strike a balance between exploration and exploitation. Forty public benchmark problem instances are used to compare the performance of our MA with that of twenty-seven existing algorithms. Our MA provides close performance for small-scale instances and much better performance for large-scale instances. It also updates more than 90% of the net set of non-dominated solutions for the large-scale instances.

A hybrid genetic local search algorithm for the permutation flowshop scheduling problem

In this paper, based on the phase-position perturbation method, an innovative optimal adaptive antenna technique is proposed, where the deduced radiation pattern formulas available for searching optimal solutions are used to search the optimal weighting vector. The optimal radiation pattern designs of adaptive antenna are studied by the phase-position perturbation method. Memetic algorithms are used to search the optimal weighting vector of the phase-position perturbations for the array factor. The design for an optimal radiation pattern of an adaptive antenna can not only adjustably suppress the interferers by placing nulls at the directions of the interfering sources, but at the same time provide a maximum main lobe in the direction of the desired signal, i.e., to maximize the signal-to-interference ratio. To achieve this goal, a new convergent method, referred to as the two-way convergent method for memetic algorithms, is proposed. The memetic algorithm combines a genetic algorithm and local search heuristics to solve combinatorial optimization problems. The memetic algorithm is a kind of improved type of the traditional genetic algorithm. By using a local search procedure, it can avoid the shortcomings of the traditional genetic algorithm, whose termination criteria are set up by using the trial and error method. This proposed method is also able to solve the multipath problem, which exists at the same time in this communication system. The optimal radiation pattern concept can be implemented in practical wireless communication systems. Simulation results are also given in this paper.

A permutation flowshop scheduling problem of optimizing the makespan and the total flow time, which can be expressed as Fm|prum|(C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">max</sub> , ΣC <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> ), is considered in this paper. An improved multi-objective memetic algorithm (IMOMA) is proposed due to the NP-hardness of the problem. In order to effectively trade-off between two objectives, we propose a NEH and LR heuristic based initialization strategy and a powerful local search strategy, in the searching framework of memetic algorithm. Finally, we perform computational experiments by solving ten largest scale instances of Taillard benchmarks, with 500 jobs and 20 machines. The results demonstrate that the proposed IMOMA outperforms the NEHFF heuristic and two state-of-the-art evolutionary multi-objective algorithms, NSGA-II and MOEA/D with respect to convergence and diversity.

A particle swarm optimization algorithm for makespan and total flowtime minimization in the permutation flowshop sequencing problem

The multiobjective time-dependent route planning problem is a hard multiobjective combinatorial optimization problem. Metaheuristics showed success in solving many hard optimization problems and recently many efforts have been directed to hybridize elements from different metaheuristics and search methods. The hybridization of genetic algorithms and local search methods proved to be successful in many domains. In this paper we present a genetic local search algorithm for solving the multiobjective time-dependent route planning problem taking the multiobjective route planning in dynamic multihop ridesharing as an example problem. The behavior of the proposed algorithm is compared, on two problem instances using a set of widely used quality indicators, with the behavior of a genetic algorithm proposed for solving the same problem. Experimentation results indicated that the proposed algorithm outperforms the genetic algorithm regarding all quality indicators.

As an emerging novel evolutionary technique, the immune algorithm has gained a lot of attention and wide applications in various fields of engineering and management. To our knowledge, this paper first considers the application of immune algorithm for the classic permutation flow shop scheduling problem. We present a hybrid heuristic combining immune algorithm and simulated annealing for the n-job, m-machine permutation flow shop scheduling problem to minimize makespan and total flow time. A heuristically directed population-based construction approach in the immune algorithm and a forward/backward shift neighborhood search heuristic in the simulated annealing of the proposed method is utilized to explore the search process for better schedules of jobs as well to speed up the convergence speed of the proposed algorithm. The proposed method is tested with Taillard’s flow shop scheduling benchmark instances for different problems with job sizes varying from 20 to 500. The computational results demonstrate that the proposed heuristic is very competitive with the state-of-the-art procedures in terms of both solution quality and computational times.

The permutation flow shop problem (PFSSP) is a well-known difficult combinatorial optimization problem. In this paper, we present a new hybrid optimization algorithm named SIGSA to solve the PFSSP. This algorithm is composed by the LRV rule, SA-based local search and IIS-based local search. First, to make GSA suitable for PFSSP, a new LRV rule based on random key is introduced to convert the continuous position in GSA to the discrete job permutation. Second, to enhance the searching capability, the SA-based local search is designed to help the algorithm to escape from local minimum. Then, the IIS-based local search is used for enhancing the individuals in GSA with a certain probability. Additionally, Comparison with other results in the literature shows that the SIGSA is an efficient and effective approach for the PFSSP.

Memetic social spider optimization algorithm for scheduling two-stage assembly flowshop in a distributed environment

The permutation flow shop problem (PFSSP) is an NP-hard problem of wide engineering and theoretical background. In this paper, a biogeography based optimization (BBO) based on memetic algorithm, named HBBO is proposed for PFSSP. Firstly, to make BBO suitable for PFSSP, a new LRV rule based on random key is introduced to convert the continuous position in BBO to the discrete job permutation. Secondly, the NEH heuristic was combined with the random initialization to initialize the population with certain quality and diversity. Thirdly, a fast local search is used for enhancing the individuals with a certain probability. Fourthly, the pair wise based local search is used to enhance the global optimal solution and help the algorithm to escape from local minimum. Additionally, simulations and comparisons based on PFSSP benchmarks are carried out, showing that our algorithm is both effective and efficient. Key words: Biogeography based optimization, permutation flow shop scheduling, memetic algorithm, local search.

Genetic local search for multi-objective combinatorial optimization

MEMPSODE: A global optimization software based on hybridization of population-based algorithms and local searches

With the rapid development of high-throughput DNA sequencing technologies, the amount of DNA sequence data is accumulating exponentially. The huge influx of data creates new challenges for storage and transmission. This paper proposes a novel adaptive particle swarm optimization-based memetic algorithm (POMA) for DNA sequence compression. POMA is a synergy of comprehensive learning particle swarm optimization (CLPSO) and an adaptive intelligent single particle optimizer (AdpISPO)-based local search. It takes advantage of both CLPSO and AdpISPO to optimize the design of approximate repeat vector (ARV) codebook for DNA sequence compression. ARV is first introduced in this paper to represent the repeated fragments across multiple sequences in direct, mirror, pairing, and inverted patterns. In POMA, candidate ARV codebooks are encoded as particles and the optimal solution, which covers the most approximate repeated fragments with the fewest base variations, is identified through the exploration and exploitation of POMA. In each iteration of POMA, the leader particles in the swarm are selected based on weighted fitness values and each leader particle is fine-tuned with an AdpISPO-based local search, so that the convergence of the search in local region is accelerated. A detailed comparison study between POMA and the counterpart algorithms is performed on 29 (23 basic and 6 composite) benchmark functions and 11 real DNA sequences. POMA is observed to obtain better or competitive performance with a limited number of function evaluations. POMA also attains lower bits-per-base than other state-of-the-art DNA-specific algorithms on DNA sequence data. The experimental results suggest that the cooperation of CLPSO and AdpISPO in the framework of memetic algorithm is capable of searching the ARV codebook space efficiently.

This paper deals with the flow shop scheduling problem. To find the optimal solution is an NP-hard problem. The paper reviews some algorithms from the literature and applies a benchmark dataset to evaluate their efficiency. In this research work, the discrete bacterial memetic evolutionary algorithm (DBMEA) as a global searcher was investigated. The proposed algorithm improves the local search by applying the simulated annealing algorithm (SA). This paper presents the experimental results of solving the no-idle flow shop scheduling problem. To compare the proposed algorithm with other researchers’ work, a benchmark problem set was used. The calculated makespan times were compared against the best-known solutions in the literature. The proposed hybrid algorithm has provided better results than methods using genetic algorithm variants, thus it is a major improvement for the memetic algorithm family solving production scheduling problems.

In the paper we present a new method for solving optimal control problems of a class of hybrid systems. We describe the new effective algorithm based on memetic algorithm (MA) for optimal control of switched systems. We concentrate on a class of problems in which a pre-specified sequence of active subsystems is known. Our aim is to find both the optimal switching instants and the optimal continuous inputs. The new approach, which we propose, decomposes the cost functional of the basic optimal control problem in Bolza form in two terms. The first term depends explicitly on a value of state variables at the final time. The second term depends on state and control trajectories. In oreder to solve those two tasks we used MA as the multi-objective optimization algorithm. In this paper we considered a fundamental bi-criteria case with two mentioned before functions: the value of state variables at the final time and the state and control trajectories. In order to find an approximation of Pareto frontier, we proposed new effective method based on genetic algorithm (GA) and local search (LS). Problems properties were taken into consideration in the design of our new approach of solving it. They were used to construct new algorithm inspired by the LS NSGA-II, which performed rather well in multi-criteria scheduling problem. Since simple genetic algorithms are efficient heuristics in searching for optimal solutions, but lack the accuracy of some more computational complex algorithms, a hybrid algorithm was constructed. It uses fast non-dominated sorting, in order to evaluate child population and allocate solutions to corresponding Pareto frontiers. I addition a local search method was used, in order to find more differentiated and better solutions. Clustering solutions from Pareto frontiers also improved diversity of solutions in child population. This approach can be used as a start point for searching for algorithm for solving optimal control problems of switched systems without pre-specified sequence of active subsystems. The performance of the algorithm is illustrated by the examples: (a) a hybrid optimal control problem with nonlinear dynamics and (b) a switched linear quadratic optimal control problem with long switching time intervals.