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This paper examines electric field optimizations at the joint of power cables by using evolution strategy [(1+1)-ES, (µ+λ)-ES] and genetic algorithm which are so- called evolutionary calculation methods. The object of the paper is to minimize the electric field strength on the inner curved electrode under the constraint that the electric field strength along the interface between the two different insulators should be below the permissible value. The surface charge method that provides accurate solutions on the boundaries for the multilayer substances with a relatively small number of elements is employed for the calculation of the electric field distribution. Three evolutionary calculation methods are employed for the optimization method. Because these methods employ mutation and rearrangement (cross-over) processes, the shape design can be performed widely and locally. As a result, each method can reduce the electric field strength on the inner curved electrode while the electric field strength along the interface was kept below the permissible value. The search results of each optimization method were compared and (µ+λ)-ES was found to have the best performance of the three methods. © 2005 Wiley Periodicals, Inc. Electr Eng Jpn, 150(4): 44–53, 2005; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.10287 [ABSTRACT FROM AUTHOR]

This paper proposes a loss minimum reconfiguration method by tabu search for an open-loop radial distribution system with distributed generators. The problem is to find the optimal normal open sectionalizing switch positions which minimize the total distribution line losses subjected to the line/transformer capacity constraints and voltage constraint. Generally, the problem is mathematically formulated as a complex combinatorial optimization problem or mixed integer programming problem, and is solved by using mathematical programming method, heuristic algorithm, intelligent method, and so on. However, a satisfactory algorithm for power companies has not yet been attained in both computational burden and solution accuracy. Thus, in this paper, the authors propose a method to solve the above problem by using tabu search (TS). Reverse power flow caused by distributed generators can be included in the solution algorithm. TS is one of the meta-heuristic algorithms, and sometimes has been evaluated to be better than genetic algorithm (GA) or simulated annealing (SA) from viewpoints of both computational speed and solution accuracy. In order to evaluate the validity and efficiency of the algorithm, several numerical examples are shown in this paper. © 2005 Wiley Periodicals, Inc. Electr Eng Jpn, 152(2): 18–25, 2005; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.20086 [ABSTRACT FROM AUTHOR]

Graph partitioning is an important problem that has extensive applications in many areas, including VLSI design, scientific computing, data mining, geographical information systems, and job scheduling. The graph partitioning problem (GPP) is NP-complete. There are several heuristic algorithms developed for finding a reasonably good solution. The most famous partitioning methods are simulated annealing (SA) and the mean field algorithm (MFA), both known to produce good partitioning for a wide class of problems, and they are used quite extensively. However, these methods are very expensive in terms of time and very sensitive to parameter tuning methods. In this paper, a new parameter-free algorithm for GPP has been proposed. The algorithm has been constructed using the S-model learning automata with multi-teacher random environments. As shown in our experiments, the proposed algorithm has some advantages over SA, MFA, and ParMeTiS. © 2004 Wiley Periodicals, Inc. Electr Eng Jpn, 148(1): 46–53, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.10310 [ABSTRACT FROM AUTHOR]

This paper presents an identification technique of parameters of a high-frequency equivalent circuit of permanent magnet synchronous motors (PMSM) driven by a PWM inverter. The high-frequency oscillatory currents such as leakage current to the motor frame (common-mode current) and high-frequency oscillatory line current (normal-mode current) are generated by switching instants of inverter transistors. The parameters of the high-frequency equivalent circuit of PMSM which can simulate the oscillating current are identified by means of genetic algorithm. It is shown that the high-frequency equivalent circuit with identified parameters can generate the oscillating current by some simulation results. © 2009 Wiley Periodicals, Inc. Electr Eng Jpn, 167(4): 57–66, 2009; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.20717 [ABSTRACT FROM AUTHOR]

The service restoration problem in distribution power systems has already been addressed with regard to this issue, for purposes such as the minimization of unrecovered load and power supply loss, considerations of priority sections, and preparation of switching procedures. However, since this involves the application of a number of methods, the related algorithms are complex. In this paper, in order to resolve this issue we first apply GA as an optimization method for investigation purposes, to simplify the algorithms used. Next, to confirm this proposed method's effects in reducing computing time, we consider the efficacy of the proposed method by using smaller numbers of generations and individual sizes than have been used in the past. The effectiveness of the proposed method is shown by the system when failure occurs. It is clear that the proposed method, consisting of GA only, is superior in terms of average fitness values and the ability to reduce computation times. © 2008 Wiley Periodicals, Inc. Electr Eng Jpn, 166(1): 10–19, 2009; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.20752 [ABSTRACT FROM AUTHOR]

This paper proposes two-layered neighborhood tabu search for determining the optimal allocation and output of D-FACTS device in a distribution system with uncertain wind power generation. In recent years, wind power generation has been introduced into distribution systems for clean and inexhaustible distributed generation. However, a distribution system with wind power generation often encounters the reverse flow due to the uncertain output performance of wind power generation. As a result, the voltage variation may become much larger. In this paper, two-layered neighborhood tabu search is proposed to determine the optimal allocation and output of D-FACTS devices. To consider the uncertainty of wind power generation, this paper carries out Monte Carlo simulation to evaluate the probabilistic voltage assessment in the distribution system with uncertain wind power generation. The proposed method is successfully applied to a sample system. © 2006 Wiley Periodicals, Inc. Electr Eng Jpn, 155(3): 29–37, 2006; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.20103 [ABSTRACT FROM AUTHOR]

This paper proposes a new VLSI placement method using genetic algorithm considering the hierarchical structure of solution space. In the proposed method, we introduce a special solution encoding which represents the hierarchical structure of solution space, and new crossover operators which can maintain the hierarchical structure of the encoded solution. Making use of the hierarchical nature of the solution space, the proposed method can search the solution space efficiently. We demonstrate the efficiency of the proposed method by applying it to VLSI cell placement problems. Computational experiments show that the proposed method obtains better placement results within less computation time compared with conventional genetic algorithm. © 2000 Scripta Technica, Electr Eng Jpn, 131(1): 86–93, 2000 [ABSTRACT FROM AUTHOR]

This paper presents an application of parallel genetic algorithms (PGA) to the optimal long-range generation expansion planning. The problem can be formulated as a combinatorial optimization problem that determines the order of introduced generation units at each interval of the year. The proposed method considers introduced power limits of each technology, maximum loads at each interval, and load duration curves at each interval. Appropriate string representation for the problem is presented. Binary and decimal coding and three selection methods are compared. The method is developed on a transputer that is one of the parallel processors. The feasibility of the proposed method is demonstrated using a typical expansion problem with four technologies and five intervals. The method is then compared with conventional dynamic programming and a simple genetic algorithm with promising results. [ABSTRACT FROM AUTHOR]

Optimal switching pattern planning is the main subject of distribution network reconfiguration to recover from a blackout or to avoid a blackout due to maintenance and construction. This is a combinatorial optimization problem, and an enumerative check of every pattern is necessary to obtain the optimal solution. In practice, successive heuristic search methods are adopted to obtain the quasi-optimal solution within a limited computation time. However, a very long computing time is still required when the problem size becomes slightly larger. The step-by-step nature of the methods results in many iterations, although it ensures satisfaction of the constraints. Reduction of the iterations is considered to be necessary to enhance processing speed. In this paper, ‘supply adequacy’ is introduced as a new criteria index to guess the solution, which is obtained by fuzzy inference for each possible pair of source and load zone. The reconfiguration pattern is obtained at once or in a few processing steps by assigning to each zone the source with the highest adequacy. Numerical examples show that the proposed method can obtain the solution faster than the ordinary method, more remarkably with the increase of problem size. [ABSTRACT FROM AUTHOR]

Among various control methods, model predictive control (MPC) is one of the major control strategies and has many successful applications. This paper presents an automatic tuning method for MPC using particle swarm optimization (PSO). One of the challenges in MPC is how control parameters can be tuned for various target plants, and the use of PSO for automatic tuning is one of the solutions. The MPC tuning problem is formulated as an optimization problem and PSO is applied as the optimization technique. PSO is one of the metaheuristic methods which are known to seek a global optimum at a relatively high ratio and with no use of a gradient. The numerical results for simple examples show the effectiveness of the proposed PSO-based automatic tuning method. © 2011 Wiley Periodicals, Inc. Electr Eng Jpn, 178(1): 40-49, 2012; Published online in Wiley Online Library (). DOI 10.1002/eej.21188 [ABSTRACT FROM AUTHOR]

This paper presents a multibranch exchange method for reconfiguration of distribution systems to reduce their line losses. In this method several switches are closed and opened simultaneously in each branch exchange operation to expand the search neighborhood. The switches to be closed are selected as the intermediate systems will be meshed configuration. Sequential branch opening method can be applied to open the switches to obtain the radial configuration efficiently. Test examples show the effectiveness of the proposed method in the case of double branch exchange. © 2010 Wiley Periodicals, Inc. Electr Eng Jpn, 174(1): 40-48, 2011; Published online in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/eej.21015 [ABSTRACT FROM AUTHOR]

This paper proposes a method for automatic design of the sensory morphology of a mobile robot. The proposed method employs two types of adaptations, ontogenetic and phylogenetic, to optimize the sensory morphology of the robot. In ontogenetic adaptation, reinforcement learning searches for the optimal policy, which is highly dependent on the sensory morphology. In phylogenetic adaptation, a genetic algorithm is used to select morphologies with which the robot can learn tasks faster. Our proposed method was applied to the design of the sensory morphology of a line-following robot. We performed simulation experiments to compare the design solution with a hand-coded robot. The results of the experiments revealed that our robot outperformed the hand-coded robot in terms of the following accuracy and learning speed, although our robot had fewer sensors than the hand-coded one. We also built a physical robot using the design solution. The experimental results revealed that this physical robot used its morphology effectively and outperformed the hand-coded robot. © 2010 Wiley Periodicals, Inc. Electr Eng Jpn, 172(1): 48–57, 2010; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.20965 [ABSTRACT FROM AUTHOR]

In a distribution system, in order to enhance the reliability of power supply, the distribution feeder is divided into several sections by installing sectionalizing switches, and then each of the sectionalized sections is connected to a different feeder. For example, one feeder is divided into three sections by two sectionalizing switches, and then each of the divided sections is connected to the other feeder through sectionalizing switch. Since a distribution system with many feeders has many sectionalizing switches, the system configuration is determined by states (opened or closed) of sectionalizing switches. Usually, a power utility tries to obtain distribution loss-minimum configuration among large numbers of configuration candidates. However, it is very difficult to determine the loss-minimum configuration such that the mathematical optimality is guaranteed, because it is well known that determination of a distribution system's configuration is to decide whether each sectionalizing switch is opened or closed by solving a combinatorial optimization problem. In this paper, the authors propose a determination method of loss-minimum configuration by which the mathematical optimality is guaranteed for a three-sectionalized three-connected distribution feeder network. A problem to determine the loss-minimum configuration is formulated as a combinatorial optimization problems with four operational constraints ( feeder capacity, voltage limit, radial structure, and three-sectionalization). In the proposed method, after picking up all partial configurations satisfied with radial structure constraint by using enumeration method, optimal combination of partial configurations is determined under the other operational constraints by using conventional optimization method. Numerical simulations are carried out for a distribution network model with 140 sectionalizing switches in order to examine the validity of the proposed algorithm in comparison with one of conventional meta-heuristics (tabu search). © 2009 Wiley Periodicals, Inc. Electr Eng Jpn, 167(1): 56– 65, 2009; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.20530 [ABSTRACT FROM AUTHOR]

This paper presents a method for cooperative allocation of step voltage regulators (SVRs) and static var compensators (SVCs) when some distributed generators (DGs) are installed in a distribution system. In the proposed method, reactive tabu search (RTS) with multiple structures and functions is applied. First, the allocations of the SVRs are selected optimally and then the tap positions of the SVRs are optimized by the RTS. Finally, the locations of the SVC are chosen in order to enhance the voltage profile in the distribution network. The proposed method enables us to take account of the installation cost of both SVRs and SVCs as an economic criterion, of the upper and lower voltage limit at each node, and also of the upper limit of line currents as constraints. By applying the proposed method to a practical distribution test system (IEEE 34 Node Test Feeder Model), it is verified that this method is efficient in allocating SVRs and SVCs at minimum cost and in regulating the system voltages within an appropriate range after introducing DGs into the distribution system. © 2008 Wiley Periodicals, Inc. Electr Eng Jpn, 164(2): 10–17, 2008; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.20639 [ABSTRACT FROM AUTHOR]

Sending voltage profile of distribution feeder is controlled by changing a tap of distribution transformer. In a distribution network with distributed generators, for reasons of effect of reversed flows from them and existence of a great number of sending voltage profile candidates, it is not easy to control sending voltage profile within the acceptable voltage limit. In this paper, in order to determine the optical sending voltage profile of distribution transformer in a distribution network with distributed generators, the authors propose a new method to determine the optimal sending voltage profile so as to minimize the total number of tap position's change per day under constraints of acceptable voltage limit. In the proposed method, after calculating acceptable range of three-phase voltage of distribution feeder, the optimal profile of tap position within the calculated acceptable voltage range is determined among these candidates by using reduced ordered binary decision diagram (ROBDD) which is an efficient enumeration algorithm. In order to check the validity of the proposed method, numerical simulations are carried out for a distribution network model with a distributed generator. © 2007 Wiley Periodicals, Inc. Electr Eng Jpn, 161(2): 16–24, 2007; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.20393 [ABSTRACT FROM AUTHOR]

In this paper, we propose a new idle time shortening method for job-shop scheduling problems (JSPs). We insert our method into a genetic algorithm (GA). The purpose of JSP is to find a schedule with the minimum makespan. We consider that it is effective to reduce idle time of a machine in order to improve the makespan. The left shift is well-known among existing algorithms for shortening idle time. The left shift cannot arrange the work to idle time. For that reason, some idle times are not shortened by left shift. We propose two kinds of algorithms which shorten such idle time. Next, we combine these algorithms and the reversal of a schedule. We apply GA with its algorithm to benchmark problems and demonstrate its effectiveness. © 2007 Wiley Periodicals, Inc. Electr Eng Jpn, 159(2): 55–63, 2007; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.20389 [ABSTRACT FROM AUTHOR]

Deep-Sub-Micron (DSM) technologies of 0.18 μm and below enable the integration of logical circuits having more than 10 million gates. In such a DSM technology, it is important to consider reducing power consumption, improving interconnection delay, and dispersing wire congestion at the initial phase of layout design. In this paper, we proposed a novel performance-driven placement algorithm. The proposed algorithm based on Genetic Algorithm (GA) has a two-level hierarchical structure. For selection control, new objective functions are introduced for reducing power consumption, improving interconnection delay, and dispersing wire congestion. Studies on floor planning and cell placement have been reported as being applications of GA to the LSI layout problem. However, no studies have ever seen the effect of applying GA with consideration of power, delay, and congestion. Results show improvement of 11.7% for the total wire length of the nets with high SW rate, 22.5% for the worst path delay, and 15.9% for wire congestion on average. © 2005 Wiley Periodicals, Inc. Electr Eng Jpn, 151(1): 56–65, 2005; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.20057 [ABSTRACT FROM AUTHOR]

In Japan, local power systems (77 kV) are served from the 275- or 154-kV substations. For enhancement of power supply reliability, the transmission lines are connected to several substations, and the operational configuration is radial. The local power system's configuration is determined by connecting and disconnecting transmission lines so as to keep the radial structure and satisfy the operation constraints. When a local power system has a number of transmission lines, many configuration candidates occur. Recently, an IEEJ committee made a practical scale local system model (IEEJ Local System Model). Since the IEEJ Local System Model has 76 transmission lines, the total number of configuration candidates is 276 (approximately 7.5 × 1022). In this paper, the authors try to strictly obtain the loss-minimum configuration under constraints such as substation capacity, line capacity, and radial structure in the IEEJ Local System Model. In order to obtain the optimal configuration, a new computation algorithm is proposed. In the proposed algorithm, the configuration determination problem is replaced as two combinatorial optimization problems based on the operational constraints [(1) substation capacity, (2) line capacity, and (3) radial structure]. One combinatorial optimization problem (subproblem 1) is to pick up all partial configurations so as to minimize total line loss under the substation capacity constraint. By using the enumeration method, subproblem 1 is solved. Subproblem 2 is solved by using the reduced ordered binary decision diagram (ROBDD). Since the proposed method is based on enumeration and Boolean function, the optimality of obtained solution is guaranteed. © 2004 Wiley Periodicals, Inc. Electr Eng Jpn, 146(4): 34–42, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.10226 [ABSTRACT FROM AUTHOR]

Unit commitment problem is an optimization problem to determine the start-up and shut-down schedule of thermal units while satisfying various constraints, for example, generation-demand balance, unit minimum up/down time, system reserve, and so on. Since this problem involves a large number of 0–1 type variables that represent up/down status of the unit and continuous variables expressing generation output, it is a difficult combinatorial optimization problem to solve. The study at present concerns the method for requiring the suboptimum solution efficiently. Unit commitment method widely used solves the problem without consideration of voltage, reactive power, and transmission constraints. In this paper, we will propose a solution of unit commitment with voltage and transmission constraints, based on the unit decommitment procedure (UDP) method, heuristic method, and optimal power flow (OPF). In this method, initial unit status will be determined from random numbers and the feasibility will be checked for minimum start-up/shut-down time and demand-generation balance. If the solution is infeasible, the initial solution will be regenerated until a feasible solution can be found. Next, OPF is applied for each time period with the temporary unit status. Then, the units that have less contribution to the cost are detected and will be shut down based on the unit decommitment rules. This process will be repeated until suboptimal solution is obtained. The proposed method has been applied to the IEEE 118-bus test system with 36 generating units with successful result. © 2003 Wiley Periodicals, Inc. Electr Eng Jpn, 144(3): 36–45, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.10187 [ABSTRACT FROM AUTHOR]

This paper proposes a new selection method of a gene from a population for reducing the number of fitness value calculations in genetic algorithms. The proposed method effectively reduces the computation time required to search for a solution to the optimization of a process involving a large number of such calculations as in robot behavior decisions and neural network learnings. In the proposed method, the use of a special buffer for storing a gene and its associated fitness value is introduced. The gene in the buffer is used as a candidate for the solution to the optimization problem. This gene is compared with a gene selected from the population, and one of the values is used in the next generation depending on the results of the comparison. The proportion of suitable genes in the population is increased in the possible shortest time. The convergence speed of our buffer depends on the population topology, which is introduced for choosing a gene from the population to compare with the buffer gene. Three kinds of topology are applied to our algorithm and they are compared and evaluated. The proposed method is applied to a robot control problem to demonstrate the validity of the technique. [ABSTRACT FROM AUTHOR]

Service restoration in distribution systems can be formulated as a combinatorial optimization problem. It is the problem to determine power sources for each load considering various operational constraints in distribution systems. Up to now, the problem has been dealt with using conventional methods such as the branch and bound method, expert systems, neural networks, and fuzzy reasoning. Recently, modern heuristic methods such as genetic algorithms (GA), simulated annealing (SA), and tabu search (TS) have been attracting notice as efficient methods for solving large combinatorial optimization problems. Moreover, reactive tabu search (RTS) can solve the parameter tuning problem, which is recognized as the essential problem of the TS. Therefore, RTS, GA, and SA can be efficient search methods for service restoration in distribution systems. This paper develops an RTS for service restoration and compares RTS, GA, and PSA (parallel SA) for the problem. The feasibility of the proposed methods is shown and compared on a typical distribution system model with promising results. © 2000 Scripta Technica, Electr Eng Jpn, 133(3): 71–82, 2000 [ABSTRACT FROM AUTHOR]

This paper proposes a new approach to plan cogeneration systems, that of distributed energy systems. The proposed approach uses structured genetic algorithms. Cogeneration systems planning provides optimal allocation of cogeneration systems, a layout of the pipeline network structure for distributing heat energy between cogeneration systems and demand areas, and optimal heat and electric energy supply to meet the energy demands. The planning is formalized as a combinatorial optimization problem with minimizing cost of energy supply as its objectives. The traditional solution method is based on mathematical programming methods. But it is difficult to get an optimal solution as the number of areas increases because of combinatorial explosion and nonlinearity. This paper describes a new method to solve the cogeneration systems planning based on genetic algorithms. The solution of the cogeneration systems planning problem has a network structure. The proposed method applies structured genetic algorithms whose genotype has a tree structure to represent a network structure. The characteristics of the proposed method are analyzed by applying the new method to empirical data of the area around station K. © 1997 Scripta Technica, Inc. Electr Eng Jpn 119(2): 26–35, 1997 [ABSTRACT FROM AUTHOR]

This paper proposes an application of genetic algorithm (GA) to contingency screening in power systems. The contingency selected by the GA-based screening method is the double line outage which has the risk of causing transient instability. Generally, the contingency screening problem including multiple outage can be interpreted as the combinatorial optimization one for searching the combination of single outages which makes the system insecure. Therefore, GA which is one of the probabilistic searches for combinatorial problems, is applicable for such contingency screening problems. In the GA-based contingency screening method, a double line outage is represented as a chromosome. The fitness of the chromosome for environment is defined by using the transient energy function of power systems. The new procedure for avoiding the loss of important outage during contingency screening is developed and embedded in the proposed method by using the theorem of schema for GA. The validity of the proposed method is confirmed by applying it to a 6-machine 30-bus 40-line system. The result shows that the GA-based contingency screening has the potential for practical use. [ABSTRACT FROM AUTHOR]

Service restoration in distribution systems can be formulated as a combinatorial optimization problem to determine power sources for each load considering radial network constraints and power source limits. Until now, the problem has been considered using conventional methods, e.g., the branch-and-bounds method, expert system, neural networks, and fuzzy reasoning. Recently, Genetic Algorithms (GA) have been recognized as one of the efficient methods for solving large combinatorial optimization problems. The method can perform parallel search, and can more easily search optimal solutions. This paper presents an application of GA to service restoration in distribution systems. The feasibility of the proposed method is demonstrated on a typical distribution system model. The result shows that the method can solve the problem, efficiently, and this tendency becomes dominant by increasing problem dimensions. [ABSTRACT FROM AUTHOR]

An immune system has powerful abilities such as memory, recognition, and learning to respond to invading antigens. The concept is expected to be applicable to many engineering applications. This paper proposes an optimization algorithm that imitates the immune system in order to solve multi-optimization problems partly by using a genetic algorithm. Through illustrative examples of multimodal functions such as the Shubert function, the proposed algorithm is shown to be effective in searching for a set of global solutions. © 1998 Scripta Technica. Electr Eng Jpn, 122(2): 30–37, 1998 [ABSTRACT FROM AUTHOR]

Tabu search is a novel technique for solving combinatorial optimization problems. The process in which the tabu search method seeks to transcend local optimality is based on an evaluation function which chooses the highest-evaluation move in terms of objective function and tabu restrictions. This paper presents a tabu search algorithm for finding a minimum-cost partition of the nodes of a directed acyclic graph into subsets of a given size, subject to the constraint that the precedence relations are satisfied. A standard tabu search approach cannot realize good solutions for this problem, because the problem is a complex multiple partitioning problem in which the number of subsets and the number of nodes in each subset are unsettled. For this problem, we use an appropriate data structure for this method and develop effective neighborhood structure and heuristics. We also assess the effectiveness of the developed algorithm. The results show that this algorithm is effective in obtaining a near-optimal solution to this problem. The running time of the procedure is proportional to the number of nodes in the graph. © 1997 Scripta Technica, Inc. Electr Eng Jpn, 119(4): 42–51, 1997 [ABSTRACT FROM AUTHOR]

Genetic algorithms (GAs) are search procedures for combinatorial optimization problems. Because GAs are based on multipoint search and use the crossover operator, they have an excellent global search ability. However, GAs are not effective for searching the solution space locally due to crossover-based search, and the diversity of the population sometimes decreases rapidly. In order to overcome these drawbacks, we propose a new algorithm called immunity-based GA (IGA), combining features of the immune system with GAs. IGA is expected to improve the local search ability of GAs and to maintain the diversity of the population. We apply IGA to the VLSI floor-plan design problem. Experimental results show that IGA performs better than GAs. © 1998 Scripta Technica, Electr Eng Jpn, 124(4): 27–36, 1998 [ABSTRACT FROM AUTHOR]

The influences of uncertainty of the load growth rate and interest rate on the total cost of low-voltage distribution facilities was investigated. We assumed that the total cost was composed of the initial construction cost, the improvement cost, and the cost of power loss. We also assumed that the distribution facilities were operated for 30 years. Genetic algorithms were used to determine how to construct cost-effective distribution facilities that did not compromise power quality. Two investment methods were compared: one in which the utility cut down the initial construction cost and improves the initial facilities along with the load growth, and one in which the utility bears the large initial cost without the improvement cost. It was found that the lower cost method depends on the load growth rate and the interest rate. It was also found that the influence of uncertainty in the load growth rate increases with an increase in the differential between the actual load growth rate and the expected one. © 2010 Wiley Periodicals, Inc. Electr Eng Jpn, 172(2): 10–19, 2010; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.20976 [ABSTRACT FROM AUTHOR]

A GA applied VRP (Vehicle Routing Problem) solving-method which realizes optimization of a cooperative logistics network is proposed. For this optimization a VRP solving-method that can obtain a human expert-level solution, which realizes steady logistics operation, in interactive response time is required. The multistage GA enables one to obtain the accurate solution under both hard and weak time constraints in interactive response time. Moreover, to realize the stable logistics operations, the daily fluctuation of shipping volume is taken into the fitness value of each individual in GA. The experimental result reveals that the proposed method obtains the accurate solution that realizes the stable operation in interactive response time. © 2009 Wiley Periodicals, Inc. Electr Eng Jpn, 168(2): 28–37, 2009; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.20855 [ABSTRACT FROM AUTHOR]

Secondary arc on solar arrays in space in defined as an arc discharge short-circuiting adjacent solar cells. As the operational voltage of spacecraft increases to 100 V, there is more risk of secondary arc that can destroy solar array circuits. Secondary arc is categorized as a vacuum arc, although its current is very low, even less than 1 A. One-dimensional modeling is carried out to derive sets of parameters that satisfy the governing equations. Genetic algorithm is used to find the optimal set of solutions. Even at a current less than 1 A, the solutions exist though the arc voltage is higher than the experimental values by several tens of volts. ©2008 Wiley Periodicals, Inc. Electr Eng Jpn, 166(4): 1– 8, 2009; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.20571 [ABSTRACT FROM AUTHOR]

We propose a parallel genetic algorithm with sexual selection. In genetic algorithms with sexual selection with one population, females keep their traits around local optima by using a lower mutation rate than that of the males, while the males change their traits actively. When a runaway process takes place, the transitions of the males' traits are biased toward a certain direction which depends on the bias of the females' preferences. If the population size is large, the search converges quickly. However, the large population size causes a decrease in search performance. In the proposed method with parallelization, the population size of each subpopulation is maintained adequately, and each subpopulation seeks its own direction of evolution independently. As a result, the proposed method makes a search converge quickly because the runaway process which leads to intermittent evolution tends to take place more quickly than one population model. We applied the proposed method to some test problems. In these problems, while the performance of conventional genetic algorithms was decreased by parallelization, the proposed method exhibited better performance with parallelization. Moreover, the performance of the proposed method is better than that of conventional methods. This capability of parallelization is a remarkable characteristic of sexual selection. © 2004 Wiley Periodicals, Inc. Electr Eng Jpn, 150(1): 42–49, 2005; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.20029 [ABSTRACT FROM AUTHOR]

For sampling various solutions from the entire Pareto front of the multiobjective resource division problem, a new Genetic Algorithm (GA) based on an evolutionary theory advocated by Kinji Imanishi is proposed. First, two types of distance between two individuals, namely, structural and functional distances, are introduced and used to define four types of relation between them, namely, homogeneous, heterogeneous, homologous, and analogous species. Then, for keeping a variety of species within a population as far as possible, a new generation alternation model with variable population size is presented. In order to find Pareto-optimal solutions effectively, a new genetic operation that combines conventional harmonic crossover with a local optimization algorithm is also proposed. Finally, the advantage of the Imanishism-based GA is demonstrated through computational experiments conducted on two- and three-objective problem instances. © 2002 Scripta Technica, Electr Eng Jpn, 139(2): 23–35, 2002; DOI 10.1002/eej.10010 [ABSTRACT FROM AUTHOR]