Hybrid Multi-objective Genetic Algorithm Research Articles

Shape Design of Lifting body Based on Genetic Algorithm

This paper briefly introduces the concept and history of lifting body, and puts forward a new method for the optimization of lifting body. This method has drawn lessons from the die line design of airplane is used to parametric numerical modeling for the lifting body, and extract the characterization of shape parameters as design variables, a combination of lifting body reentry vehicle aerodynamic conditions, aerodynamic heating, volumetric Rate and the stability of performance. Multi-objective hybrid genetic algorithm is adopted to complete the aerodynamic shape optimization and design of hypersonic lifting body vehicle when under more variable and constrained condition in order to obtain the Pareto optimal solution of Common Aero Vehicle shape.

Sequencing the Reconfigurable Assembly Line with a Hybrid Multi-Objective Genetic Algorithm

In order to solve the reconfigurable assembly line sequencing problem, a multi-objective optimization mathematical model is presented, which includes three practically important objectives. Such as minimizing the total utility work cost, minimizing the total production rate variation and minimizing reconfigurable setup cost are considered. A scheduling method for reconfigurable assembly line is proposed based on Pareto multi-objective genetic algorithm, In order to ensure the group’s variety, prevent the premature convergence problem and enhance the globe-optimization capability, some key technologies such as population ranking method, Niche technique are applied. The adaptive crossover and mutation probabilities methods are developed. The computational results show that the proposed hybrid algorithm finds solutions with better quality especially in the case of large-sized problems.

Deriving Optimal Refill Rules for Multi-Purpose Reservoir Operation

This paper focuses on deriving optimal refill rules for a multi-purpose reservoir, and aims to maximize utilization benefits under the condition of flood control safety. The entire flood season is divided into multiple sub-seasons (i.e. pre-flood season, main-flood season and post-flood season). By advancing the start of the refill period to the beginning of the post-flood season, seasonal design flows during the new refill period are estimated. A multi-objective refill operation model is proposed by combining flood control and conservation together. The simulation–optimization-test framework and hybrid multi-objective genetic algorithms are developed and used to optimize the rule curves. China’s Three Gorges Reservoir is selected as a case study and the application results show that the proposed model can increase the hydropower generation by 17.4%, decrease spilled water by 43.9%, and improve the refill probability greatly without decreasing the flood control standard and navigation probability during the refill period.

An effective hybrid multi-objective genetic algorithm for bi-criteria scheduling on a single batch processing machine with non-identical job sizes

This paper addresses the problem of scheduling jobs with non-identical sizes on a single batch processing machine. A batch processing machine is one which can process multiple jobs simultaneously as a batch as long as the total size of jobs being processed does not exceed the machine capacity. The batch processing time is equal to the longest processing time among all jobs in the batch. For the simultaneous minimization of the bi-criteria of makespan and maximum tardiness, we propose two different multi-objective genetic algorithms based on different representation schemes. While the first algorithm do search via generating sequences of jobs using genetic operators and then batching jobs keeping their order in the sequence, the second algorithm uses the idea of generating batches of jobs directly using genetic operators and ensures feasibility through using heuristic procedures. The type of representation used in the second algorithm allows introducing heuristics with the ability of biasing the search towards each objective and also allows hybridization with a local search heuristic that gives the ability of finding Pareto-optimal or locally efficient Pareto-solutions. Computational results show that the non-dominated solutions obtained by the latter algorithm are very superior in closeness to the true Pareto-optimal solutions and to keep diversity in the obtained Pareto-set, as the problem size increases.

An improved hybrid multi-objective parallel genetic algorithm for hybrid flow shop scheduling with unrelated parallel machines

In this paper, we address the hybrid flow shop scheduling problems with unrelated parallel machines, sequence-dependent setup times and processor blocking to minimize the makespan and maximum tardiness criteria. Since the problem is strongly NP-hard, we propose an effective algorithm consisting of independent parallel genetic algorithms by dividing the whole population into multiple subpopulations. Each subpopulation will be assigned with different weights to search for optimal solutions in different directions. To further cover the Pareto solutions, each algorithm is combined with a novel local search step and a new helpful procedure called Redirect. The proposed Redirect procedure tries to help the algorithm to overcome the local optimums and to further search the solution space. When a population stalls over a local optimum, at first, the algorithm tries to achieve a better solution by implementing the local search step based on elite chromosomes. As implementing the local search step is time-consuming, we propose a method to speed up the searching procedure and to further increase its efficiency. If the local search step failed to work, then the Redirect procedure changes the direction and refreshes the population. Computational experiments indicate that our proposed improving procedures are thriving in achieving better solutions. We have chosen two measures to evaluate the performance of our proposed algorithms. The obtained results clearly reveal the prosperity of our proposed algorithm considering both measures we have chosen.

Multi-objective hybrid genetic algorithm for quay crane dynamic assignment in berth allocation planning

The development of containers' transportation has maintained a high momentum since 1961, especially, the containers' traffic growth reached 10---11% in recent years. Container terminals play an important role in the transportation chain, in order to response rapidly for the requirement of modern logistics, better resource allocation, lower cost, and higher operation efficiency are needed. In this paper, we introduce the quay crane dynamic assignment (QCDA) in berth allocation planning problem (BAP) and formulate a multi-objective mathematical model considering each berth for container ship with QCDA and number of Quay Crane's Move. In order to solve this QCDA in BAP problem, we propose a multi-objective hybrid Genetic Algorithm approach with a priority-based encoding method. To demonstrate the effectiveness of proposed mohGA approach, numerical experiment is carried out and the best solution to the problem is obtained.

Optimization of short-haul aircraft schedule recovery problems using a hybrid multiobjective genetic algorithm

A hybrid multiobjective genetic algorithm is presented in this paper to find an efficient solution for the daily short-haul aircraft schedule recovery problems which usually happen due to some disturbance events and require a time-sensitive solution to meet various hard constraints and soft objectives. The proposed algorithm employs an adaptive evaluated vector (AEV) to guide the solution search and uses the method of inequality-based multiobjective genetic algorithm to provide the multiobjective solution. A simulated disturbance experiment, temporal airport closure, is made and shown that the hybrid method can provide a very efficient short-haul schedule recovery solution under various performance indices.

Multiobjective hybrid genetic algorithm for quay crane scheduling in berth allocation planning

With the development of the global business and logistics under the internet environment a Container Terminal (CT) system becomes more and more busy. Therefore, the available resources in the seaport get scarcer than before. In order to increase the operating efficiency of CT system, the resources planning problem has become a critical issue in the fields of operations research and logistics. In this paper, we introduce the Berth Allocation Planning (BAP) problem and formulate a multiobjective mathematical model considering each berth for container ship with different number of Quay Cranes (QCs) and balance of QC's workload. In order to solve this QC scheduling in BAP problem, we propose a multiobjective hybrid Genetic Algorithm (mohGA) approach with a priority-based encoding method. To demonstrate the effectiveness of proposed mohGA approach, numerical experiment is carried out and the best solution to the problem is obtained.

Evolutionary-Based Multidisciplinary Design Exploration for Silent Supersonic Technology Demonstrator Wing

Multidisciplinary design exploration with multiple objectives was performed for the wing shape of a silent supersonic technology demonstrator, considering aerodynamics, structures, and boom noise. Aerodynamic evaluation was carried out by solving Euler equations with computational fluid dynamics, and composite structural evaluation was performed by using Nastran for strength and vibration requirements with computational structural dynamics. The intensity of the sonic boom was evaluated by a modified linear theory. The optimization problem had five objective functions: minimization of the pressure and friction drags, boom intensity at the supersonic condition, and composite structural weight and maximization of the lift at the subsonic low-speed condition. The three-dimensional wing shape defined by 58 design variables was optimized with multi-objective particle swarm optimization and an adaptive-range multi-objective hybrid genetic algorithm method. In the structural evaluation, the combination optimization of stacking sequences of laminated composites was performed for inboard and outboard wings with strength and vibration requirements. Consequently, 75 nondominated solutions were efficiently obtained through 12 generations. Moreover, data mining was performed to obtain the design knowledge for deciding a compromise solution. The data mining revealed useful knowledge in the design space, such as the tradeoff information among the objective functions and the correlations between the objective functions and design variables. A compromise solution was successfully determined by using the obtained physical design knowledge.

Pareto optimal synthesis of four-bar mechanisms for path generation

Abstract In this paper, hybrid multi-objective genetic algorithms (GAs) are used for Pareto optimum synthesis of four-bar linkages considering the minimization of two objective functions simultaneously. The important conflicting objective functions that have been considered in this work are tracking error (TE) and transmission angle’s deviation from 90° (TA). The obtained Pareto fronts demonstrate that trade-offs between these two objectives can be recognized so that a designer can optimally compromise for the selection of a desired four-bar linkage. Moreover, the obtained optimum points are also compared with those given in the literature in terms of both tracking error (TE) and transmission angle error (TA).

Multi-criteria human resource allocation for solving multistage combinatorial optimization problems using multiobjective hybrid genetic algorithm

Multi-criteria human resource allocation involves deciding how to divide human resource of limited availability among multiple demands in a way that optimizes current objectives. In this paper, we focus on multi-criteria human resource allocation for solving multistage combinatorial optimization problem. Hence we tackle this problem via a multistage decision-making model. A multistage decision-making model is similar to a complex problem solving, in which a suitable sequence of decisions is to be found. The task can be interpreted as a series of interactions between a decision maker and an outside world, at each stage of which some decisions are available and their immediate effect can be easily computed. Eventually, goals would be reached due to the found of optimized variables. In order to obtain a set of Pareto solutions efficiently, we propose a multiobjective hybrid genetic algorithm (mohGA) approach based on the multistage decision-making model for solving combinatorial optimization problems. According to the proposed method, we apply the mohGA to seek feasible solutions for all stages. The effectiveness of the proposed algorithm was validated by its application to an illustrative example dealing with multiobjective resource allocation problem.

Multiobjective resource allocation problem by multistage decision-based hybrid genetic algorithm

Multiobjective resource allocation problem (MORAP) is the process of allocating resources among the various projects or business units to meet the expected objectives. Resources may be manpower, assets, raw materials, capital or anything else in limited supply which can be used to accomplish the goals. The goal may be objectives or targets (i.e., maximizing profits, minimizing costs, or achieving the best possible quality), usually driven by specific future needs. For this reason, the MORAP will be formulated as a complex multiobjective optimization model. Hence, we tackle this problem via a multistage decision making model. A multistage decision making model is similar to a complex problem solving, in which a suitable sequence of decisions is to be found. The task can be interpreted as a series of interactions between a decision maker and an outside world, at each stage of which some decisions are available and their immediate effect can be easily computed. Eventually, goals would be reached due to the found of optimized variables. In this paper, in order to obtain a set of Pareto solutions efficiently, we propose a multiobjective hybrid genetic algorithm (mo-hGA) approach based on the multistage decision making model. According to the proposed method, we apply the mo-hGA to seek feasible solutions for all stages. The effectiveness of the proposed algorithm was validated by its application to an illustrative example dealing with multiobjective resource allocation problem.

BI-CRITERIA SCHEDULING ON A BATCHING MACHINE WITH NON-IDENTICAL JOB SIZES

This paper addresses scheduling jobs with non-identical sizes on a single batch-processing machine. Two different multi-objective genetic algorithms (MOGA) have been proposed. The first one is a sequence based MOGA (SMOGA) that generates random sequences of jobs and applies the batch first fit heuristic to group the jobs. The second one is a batch based hybrid MOGA (BHMOGA) that generates random batches of jobs and ensures feasibility through using knowledge of the problem. Computational results show that non-dominated solutions obtained by BHMOGA are superior in closeness to the true Pareto-optimal solutions and to keep diversity in the obtained Pareto-set, as the problem size increases.

Scheduling algorithm for real-time tasks using multiobjective hybrid genetic algorithm in heterogeneous multiprocessors system

The scheduling problem for real-time tasks on multiprocessor is one of the NP-hard problems. This paper proposes a new scheduling algorithm for real-time tasks using multiobjective hybrid genetic algorithm (mohGA) on heterogeneous multiprocessor environment. In solution algorithms, the genetic algorithm (GA) and the simulated annealing (SA) are cooperatively used. In this method, the convergence of GA is improved by introducing the probability of SA as the criterion for acceptance of new trial solution. The proposed algorithm has a multiobjective to minimize the total tardiness and completion time simultaneously. For these conflicting objectives, this paper combines adaptive weight approach (AWA) that utilizes some useful information from the current population to readjust weights for obtaining a search pressure toward a positive ideal point. The effectiveness of the proposed algorithm is shown through simulation studies. In simulation studies, the results of the proposed algorithm are better than that of other algorithms.

Implementation of Simple Multiobjective Memetic Algorithms and Its Application to Knapsack Problems

The aim of this paper is to propose a simple but powerful multiobjective hybrid genetic algorithm and to examine its search ability through computational experiments on commonly used test problems in the literature. We first propose a new multiobjective hybrid genetic algorithm, which is designed by combining local search with an EMO (evolutionary multiobjective optimization) algorithm. In the design of our algorithm, we try to make its algorithmic complexity as simple as possible so that it can be easily understood, easily implemented and easily executed within short CPU time. At the same time, we try to maximize its search ability. Our algorithm makes use of advantages of both EMO and local search for achieving high search ability without increasing its algorithmic complexity. For example, each solution is evaluated based on Pareto ranking and the concept of crowding as in many EMO algorithms. On the other hand, a weighted scalar fitness function is used for efficiently executing local search. A kind of elitism is also implemented using Pareto ranking in the process of generation update. Through computational experiments on multiobjective 0/1 knapsack problems, we examine the search ability of four variants of our algorithm with various parameter specifications. Those variants are different from each other in the implementation of parent selection and local search. While some variants use the weighted scalar fitness function only for local search, others use it for both local search and parent selection. One variant uses Pareto ranking instead of the weighted scalar fitness function in local search. In addition to the comparison among those four variants, our algorithm is also compared with well-known EMO algorithms (i.e., SPEA of Zitzler & Thiele and NSGA-II of Deb et al.) and memetic EMO algorithms (i.e., M-PAES of Knowles & Corne and MOGLS of Jaszkiewicz). We also examine the effect of the balance between genetic search and local search on the search ability of our algorithm using two parameters: a local search application probability and a local search stopping condition. Moreover we demonstrate the usefulness of a weighted scalar fitness function-based greedy repair procedure in the application of memetic EMO algorithms to multiobjective 0/1 knapsack problems. Our experimental results by various EMO algorithms show that there exists a clear tradeoff between CPU time and the quality of solution sets obtained by each algorithm. Since our algorithm is very simple, it can be efficiently executed. As a result, our algorithm outperforms many EMO and memetic EMO algorithms in terms of CPU time for large test problems while it does not always outperform them in terms of the quality of obtained solution sets.

Implementation of Simple Multiobjective Memetic Algorithms and Its Application to Knapsack Problems

The aim of this paper is to propose a simple but powerful multiobjective hybrid genetic algorithm and to examine its search ability through computational experiments on commonly used test problems ...

A MULTI-OBJECTIVE HEURISTIC-BASED HYBRID GENETIC ALGORITHM1*

ABSTRACT Most attempts by researchers to improve upon multi-objective genetic algorithms (MOGAs) involve different implementations of the traditional genetic algorithm operations (i.e., fitness assignment, crossover, and mutation). However, adherence to models that remain consistent with evolutionary theory may be stifling further performance gains that could be realized through the development of hybrid algorithms. This paper presents a hybrid MOGA that combines a baseline MOGA with heuristics specifically tailored to address deficiencies often encountered in multi-objective optimization. The new hybrid technique is compared to the baseline MOGA in its application to a two-objective two-bar truss design and a three-objective packaging design of a power electronic module. The comparison is aided by four recently developed metrics that provide a balanced quantitative measurement of performance. The new technique was shown to consistently outperform the baseline MOGA for the application examples. *Communicated by S. Velinsky.

A multiobjective hybrid genetic algorithm for the capacitated multipoint network design problem

The capacitated multipoint network design problem (CMNDP) is NP-complete. In this paper, a hybrid genetic algorithm for CMNDP is proposed. The multiobjective hybrid genetic algorithm (MOHGA) differs from other genetic algorithms (GAs) mainly in its selection procedure. The concept of subpopulation is used in MOHGA. Four subpopulations are generated according to the elitism reservation strategy, the shifting Prufer vector, the stochastic universal sampling, and the complete random method, respectively. Mixing these four subpopulations produces the next generation population. The MOHGA can effectively search the feasible solution space due to population diversity. The MOHGA has been applied to CMNDP. By examining computational and analytical results, we notice that the MOHGA can find most nondominated solutions and is much more effective and efficient than other multiobjective GAs.

Optimization of beam orientation in radiotherapy using planar geometry

This paper proposes a new geometrical formulation of the coplanar beam orientation problem combined with a hybrid multiobjective genetic algorithm. The approach is demonstrated by optimizing the beam orientation in two dimensions, with the objectives being formulated using planar geometry. The traditional formulation of the objectives associated with the organs at risk has been modified to account for the use of complex dose delivery techniques such as beam intensity modulation. The new algorithm attempts to replicate the approach of a treatment planner whilst reducing the amount of computation required. Hybrid genetic search operators have been developed to improve the performance of the genetic algorithm by exploiting problem-specific features. The multiobjective genetic algorithm is formulated around the concept of Pareto optimality which enables the algorithm to search in parallel for different objectives. When the approach is applied without constraining the number of beams, the solution produces an indication of the minimum number of beams required. It is also possible to obtain non-dominated solutions for various numbers of beams, thereby giving the clinicians a choice in terms of the number of beams as well as in the orientation of these beams.