• Journal of Information Processing Systems
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• v.16 no.1
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• pp.210-223
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• 2020

Since constrained optimization algorithms are easy to fall into local optimum and their ability of searching are weak, an improved symbiotic organisms search algorithm with mixed strategy based on adaptive ε constrained (ε_SOSMS) is proposed in this paper. Firstly, an adaptive ε constrained method is presented to balance the relationship between the constrained violation degrees and fitness. Secondly, the evolutionary strategies of symbiotic organisms search algorithm are improved as follows. Selecting different best individuals according to the proportion of feasible individuals and infeasible individuals to make evolutionary strategy more suitable for solving constrained optimization problems, and the individual comparison criteria is replaced with population selection strategy, which can better enhance the diversity of population. Finally, numerical experiments on 13 benchmark functions show that not only is ε_SOSMS able to converge to the global optimal solution, but also it has better robustness.

• Journal of the Korea Society of Computer and Information
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• v.24 no.1
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• pp.167-178
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• 2019

In this paper, we study the multi-objectives vehicle and drone routing problem with time windows, MOVDRPTW for short, which is defined in an urban delivery network. We consider the dual modal delivery system consisting of drones and vehicles. Drones are used as a complement to the vehicle and operate in a point to point manner between the depot and the customer. Customers make various requests. They prefer to receive delivery services within the predetermined time range and some customers require fast delivery. The purpose of this paper is to investigate the effectiveness of the delivery strategy of using drones and vehicles together with a multi-objective measures. As experiment datasets, we use the instances generated based on actual courier delivery data. We propose a hybrid multi-objective evolutionary algorithm for solving MOVDRPTW. Our results confirm that the vehicle-drone mixed strategy has 30% cost advantage over vehicle only strategy.

• Journal of Korea Game Society
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• v.11 no.2
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• pp.57-68
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• 2011

This study approaches how newly emerged game companies has implemented strategies for product diversification according to market growth cycle(beginninggrowing-mature) by empirical case study through evolutionary theory and resource based theory approach. At the beginning, online game companies had grown with different strategies(technology based, service based) by initial condition(genre, technological level, user attribute). After market growth, for product diversification, these companies carried out path-dependent alliance strategy(complementary, competitive) depending on resource base(technology capacity, service capacity based). As online game market getting mature, these companies has adapted flexibly in responding to market growth cycle by integrated strategy(naturally selected to mobilize every possible resource capability). By analyzing the alliance strategies pattern of online game companies in newly emerged game industry according to market growth cycle through combination of resource based theory and evolutionary theory, these results suggest that new industrial, theoretical, policy model is required.

• Journal of Electrical Engineering and Technology
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• v.1 no.4
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• pp.435-447
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• 2006

This paper presents an application of the parallel Adaptive Evolutionary Algorithm (AEA) to search an optimal solution of the service restoration in electric power distribution systems, which is a discrete optimization problem. The main objective of service restoration is, when a fault or overload occurs, to restore as much load as possible by transferring the de-energized load in the out of service area via network reconfiguration to the appropriate adjacent feeders at minimum operational cost without violating operating constraints. This problem has many constraints and it is very difficult to find the optimal solution because of its numerous local minima. In this investigation, a parallel AEA was developed for the service restoration of the distribution systems. In parallel AEA, a genetic algorithm (GA) and an evolution strategy (ES) in an adaptive manner are used in order to combine the merits of two different evolutionary algorithms: the global search capability of the GA and the local search capability of the ES. In the reproduction procedure, proportions of the population by GA and ES are adaptively modulated according to the fitness. After AEA operations, the best solutions of AEA processors are transferred to the neighboring processors. For parallel computing, a PC cluster system consisting of 8 PCs was developed. Each PC employs the 2 GHz Pentium IV CPU and is connected with others through switch based fast Ethernet. To show the validity of the proposed method, the developed algorithm has been tested with a practical distribution system in Korea. From the simulation results, the proposed method found the optimal service restoration strategy. The obtained results were the same as that of the explicit exhaustive search method. Also, it is found that the proposed algorithm is efficient and robust for service restoration of distribution systems in terms of solution quality, speedup, efficiency, and computation time.

• International Journal of Naval Architecture and Ocean Engineering
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• v.8 no.6
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• pp.602-614
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• 2016

It is necessary to develop an efficient optimization technique to perform optimum designs which have given design spaces, discrete design values and several design goals. As optimization techniques, direct search method and stochastic search method are widely used in designing of ship structures. The merit of the direct search method is to search the optimum points rapidly by considering the search direction, step size and convergence limit. And the merit of the stochastic search method is to obtain the global optimum points well by spreading points randomly entire the design spaces. In this paper, Pareto Strategy (PS) multi-objective function method is developed by considering the search direction based on Pareto optimal points, the step size, the convergence limit and the random number generation. The success points between just before and current Pareto optimal points are considered. PS method can also apply to the single objective function problems, and can consider the discrete design variables such as plate thickness, longitudinal space, web height and web space. The optimum design results are compared with existing Random Search (RS) multi-objective function method and Evolutionary Strategy (ES) multi-objective function method by performing the optimum designs of double bottom structure and double hull tanker which have discrete design values. Its superiority and effectiveness are shown by comparing the optimum results with those of RS method and ES method.

• Korean Journal of Biological Psychiatry
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• v.29 no.1
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• pp.1-8
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• 2022

Objectives In regard to the social competition hypothesis, depression is viewed as an involuntary defeat strategy. A previous study has demonstrated that adaptation in microenvironments can result in a wide range of behavioural patterns including defense activation disorders. Using a simulation model with evolutionary ecological agents, we explore how the fitness of various defence activation traits has changed over time in different environments with high and low social mobility. Methods The Evolutionary Ecological Model of Defence Activation Disorder, which is based on the Marginal Value Theorem, was used to examine changes in relative fitness for individuals with defensive activation disorders after adjusting for social mobility. Results Our study examined the effects of social mobility on fitness by varying the d-values, a measure of depression in the model. With a decline in social mobility, the level of fitness of individuals with high levels of defense activation decreased. We gained insight into the evolutionary influence of varying levels of social mobility on individuals' degrees of depression. In the context of a highly stratified society, the results support a mismatch hypothesis which states that high levels of defence are detrimental. Conclusions Despite the fact that niche specialization in habitats composed of multiple microenvironments can result in diverse levels of defensive activation being evolutionary strategies for stability, decreased social mobility may lead to a decrease in fitness of individuals with highly activated defence modules. There may be a reason behind the epidemic of depression in modern society.

• Transactions on Control, Automation and Systems Engineering
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• v.2 no.1
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• pp.13-18
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• 2000

In this paper, an Adaptive Evolutionary Computation(AEC) is proposed. AEC uses a genetic algorithm(GA) and an evolution strategy (ES) in an adaptive manner is order to take merits of two different evolutionary computations: global search capability of GA and local search capability of ES. In the reproduction procedure, proportions of the population by GA and ES are adaptively modulated according to the fitness. AEC is used to design the membership functions and the scaling factors of fuzzy logic controller (FLC). To evaluate the performances of the proposed FLC, we make an experiment on FLC for the speed control of an actual DC series motor system with nonlinear characteristics. Experimental results show that the proposed controller has better performance than that of PD controller.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.12
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• pp.1498-1506
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• 1999

In this paper, a hybridization of Evolutionary Strategy (ES) and a Two-Phase Neural Network(TPNN) is applied to the optimal environmental and economic operation. As the evolutionary computation, ES is to search for the global optimum based on natural selection and genetics but it shows a defect of reducing the convergence rate in the latter part of search, and often does not search the exact solution. Also, neural network theory as a local search technique can be used to search a more exact solution. But it also has the defect that a solution frequently sticks to the local region. So, new algorithm is presented as hybrid methods by combining merits of two methods. The hybrid algorithm has been tested on Emission Constrained Economic Dispatch (ECED) problem and Weighted Emission Economic Dispatch (WEED) problem for optimal environmental and economic operation. The result indicated that the hybrid approach can outperform the other computational efficiency and accuracy.

• Journal of Ecology and Environment
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• v.31 no.3
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• pp.177-181
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• 2008

Recently, the evolutionary study of human psychology and behavior has undergone rapid growth, diversifying into a few distinct sub-disciplines. One fundamental issue over which researchers in Human Behavioral Ecology and Evolutionary Psychology (EP) have different views is the role of formal optimality modeling for making hypotheses and deriving predictions about human adaptations. The study of EP typically rests on informal inferences and rarely uses optimality modeling, a strategy which human behavioral ecologists have severely criticized. Here I argue that EP researchers have every reason to make extensive use of optimality modeling as its research method. I show that optimality modeling can play an integral role in identifying the functional organization of human psychological adaptations.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2008.10a
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• pp.191-196
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• 2008

This paper presents an evolutionary algorithm for goal programming with preemptive priority. To do this, an evolutionary strategy is suggested which search for the solution satisfying the goals in the order of the priority. Two-sided assembly line balancing problems with multiple goals are used to validate the applicability of the algorithm. In the problems, three goals are considered in the following priority order: minimizing the number of mated-stations, achieving the goal level of workload smoothness, and maximizing the work relatedness. The proper evolutionary components such as encoding and decoding method, evaluation scheme, and genetic operators, which are specific to the problem being solved, are designed in order to improve the algorithm's performance. The computational result is reported.