Showing papers on "Evolutionary programming published in 2021"

Meta-heuristic approach for solving multi-objective path planning for autonomous guided robot using PSO–GWO optimization algorithm with evolutionary programming

Abstract: As path planning is an NP-hard problem it can be solved by multi-objective algorithms. In this article, we propose a multi-objective path planning algorithm which consists of three steps: (1) the first step consists of optimizing a path by the hybridization of the Grey Wolf optimizer-particle swarm optimization algorithm, it minimizes the path distance and smooths the path. (2) the second step, all optimal and feasible points generated by PSO–GWO algorithm are integrated with Local Search technique to convert any infeasible point into feasible point solution, the last step (3) depends on collision avoidance and detection algorithm, where mobile robot detects the presence of an obstacle in its sensing circle and then avoid them using collision avoidance algorithm. The proposed method is further improved by adding the mutation operators by evolutionary, it further solves path safety, length, and smooths it further for a mobile robot. Different simulations have been performed under numerous environments to test the feasibility of the proposed algorithm and it is shown the algorithm produces a more feasible path with a short distance and thus proves that it overcomes the shortcomings of other conventional techniques.

Sustainable pavement maintenance and rehabilitation planning using differential evolutionary programming and coyote optimisation algorithm

Abstract: CO2 emission reduction in large-scale pavement network maintenance planning has been an immense concern. The conventional single-objective optimisation modelsoverlook environmental issues such as C...

Modified multi-objective evolutionary programming algorithm for solving project scheduling problems

Abstract: Though the Genetic Algorithm (GA) has received considerable attention recently in solving multi-objective optimization problems, inefficiency regarding performance has been reported in applications related to project scheduling. The degradation in efficiency was magnificent in applications of highly epistatic objective functions, including scheduling problems wherein the parameters being optimized are highly correlated. Furthermore, the crossover, being the dominant operator in GA, added significantly to the observed inefficiency for causing violations in the dependency between activities. Unlike GA, the Evolutionary Programming (EP) algorithm employs only a mutation operator which makes it less vulnerable to the dependency violation issue. This study proposes a modified Multi-Objective Evolutionary Programming (MOEP) algorithm to model and solve scheduling problems of multi-mode activities, including time–cost trade-off and finance-based scheduling with resource levelling. The modification involves the implementation of a new mutation operator to accommodate the scheduling problems in hand. Furthermore, the modified MOEP algorithm is benchmarked against the two multi-objective algorithms of SPEA-II and NSGA-II which have been used extensively in the literature to solve project scheduling problems. The results indicated that the modified MOEP algorithm outperformed SPEA-II and NSGA-II in terms of the diversity and quality of the Pareto optimal set.

A Multiscale Optimization Technique for Large-Scale Subsurface Profiling

Abstract: An efficient multiresolution inverse scattering approach is presented for profiling high-contrast buried targets in large investigation domains (IDs). The proposed technique is based on an iterative multiscale approach (IMSA) that starts with coarse meshes and successively zooms in and marches toward the detected target location. Hence, by tightening the ID, the resolution could be enhanced without any increase in the number of meshes of the search domain. Here, the global evolutionary programming (EP) optimization algorithm is used in each step of IMSA, to guarantee the success of inversion process. In particular, an efficient variation in EP with Cauchy mutation is implemented for enhanced convergence. Moreover, a hybrid combination of multifrequencies (MFs) and frequency hopping (FH) schemes is combined with the proposed technique to better deal with the problem’s nonlinearity and also simultaneously make a consistent trade-off between mesh length and frequencies. The proposed FH-MF-IMSA is evaluated by implementing it to various large-scale subsurface problems. Furthermore, the superior performance of this technique is shown by comparing its results with the standard IMSA, as well as with other well-known global optimization techniques.

Multi-area economic dispatching using improved grasshopper optimization algorithm

Abstract: In this study a new optimization algorithm is presented to solve the multi-area economic dispatching (MAED) problem. The studied system includes the tie-line constraints including transmission losses, prohibited operating zones, multiple fuels, and valve-point loading. For optimized solving the MAED algorithm, grasshopper optimization algorithm (GOA) is utilized. GOA is a newly introduced swarm-based optimization algorithm that is inspired by the swarming behavior of the grasshopper insects. For validating the proposed method, it is compared with three different case studies and the results have been compared with some different meta-heuristics such as particle swarm optimization, artificial bee colony, evolutionary programming, and differential evolution to demonstrate the capability of the presented system to solve the MAED problems.

Dynamic economic emission dispatch using whale optimization algorithm for multi-objective function

Abstract: Introduction. Dynamic Economic Emission Dispatch is the extended version of the traditional economic emission dispatch problem in which ramp rate is taken into account for the limit of generators in a power network. Purpose. Dynamic Economic Emission Dispatch considered the treats of economy and emissions as competitive targets for optimal dispatch problems, and to reach a solution it requires some conflict resolution. Novelty. The decision-making method to solve the Dynamic Economic Emission Dispatch problem has a goal for each objective function, for this purpose, the multi-objective problem is transformed into single goal optimization by using the weighted sum method and then control/solve by Whale Optimization Algorithm. Methodology. This paper presents a newly developed metaheuristic technique based on Whale Optimization Algorithm to solve the Dynamic Economic Emission Dispatch problem. The main inspiration for this optimization technique is the fact that metaheuristic algorithms are becoming popular day by day because of their simplicity, no gradient information requirement, easily bypass local optima, and can be used for a variety of other problems. This algorithm includes all possible factors that will yield the minimum cost and emissions of a Dynamic Economic Emission Dispatch problem for the efficient operation of generators in a power network. The proposed approach performs well to perform in diverse problem and converge the solution to near best optimal solution. Results. The proposed strategy is validated by simulating on MATLAB® for 5 IEEE standard test system. Numerical results show the capabilities of the proposed algorithm to establish an optimal solution of the Dynamic Economic Emission Dispatch problem in a several runs. The proposed algorithm shows good performance over the recently proposed algorithms such as Multi-Objective Neural Network trained with Differential Evolution, Particle swarm optimization, evolutionary programming, simulated annealing, Pattern search, multi-objective differential evolution, and multi-objective hybrid differential evolution with simulated annealing technique.

Comparison study on nature-inspired optimization algorithms for optimization back analysis of underground engineering

Abstract: Optimization back analysis is the most common approach to displacement back analysis for underground engineering. However, this is a non-convex problem that requires the use of nature-inspired global optimization algorithms. Therefore, the present study will investigate on the suitability of six state-of-the-art nature-inspired algorithms for elastic back analysis and elastic–plastic back analysis. These algorithms include improved genetic algorithm, immunized evolutionary programming, particle swarm optimization, continuous ant colony optimization, artificial bee colony and black hole algorithm. Numerical results indicate that immunized evolutionary programming is overall the best algorithm followed by the black hole algorithm; while, the improved genetic algorithm is the worst optimizer. Meanwhile, using elastic back analysis, the sensitivity analysis of the main input parameters for these nature-inspired optimization algorithms has been conducted. At last, the comparative results have been verified by using in one real underground roadway in Huainan coal mine of China.

Chaotic Evolutionary Programming for an Engineering Optimization Problem

Abstract: The aim of the current paper is to present a mimetic algorithm called the chaotic evolutionary programming Powell’s pattern search (CEPPS) algorithm for the solution of the multi-fuel economic load dispatch problem. In the CEPPS algorithm, the exploration process is maintained by chaotic evolutionary programming, whereas exploitation is taken care off by a pattern search. The proposed CEPPS has two variants based on the Gauss map and the tent map. Seven generalized benchmark test functions and six cases of the multi-fuel economic load dispatch problem are considered for the performance analysis. It is observed from the analysis that the CEPPS solution procedure based on the tent map exhibits superiority to obtain an excellent solution and better convergence characteristics than traditional chaotic evolutionary programming. Further, the performance investigation for the considered economic load dispatch shows that the Gauss map CEPPS solution procedure performs better than the tent map based CEPPS to obtain the solution of the multi-fuel economic dispatch problem.

A Non-Convex Economic Dispatch Problem with Point-Valve Effect Using a Wind-Driven Optimisation Approach

Abstract: This study presents the efficiency of the wind-driven optimisation (WDO) approach in solving non-convex economic dispatch problems with point-valve effect. The best economic dispatch for a power system is one wherein the system can generate energy at a low cost. The calculation of the generating cost is subject to a number of constraints, such as the power demand for the entire system and the generation limit for each generator unit in the system. In addition, the system should also produce low power loss. The WDO optimisation technique is developed based on the concept of natural wind movement, which serves as a stabiliser to equalise the inequality of air pressure in the atmosphere. One major advantage of WDO over other techniques is its search accuracy. The proposed algorithm has been implemented in two systems, namely, the 10-generator and 40-generator systems. Both systems were tested in a Matlab environment. To highlight the capabilities of WDO, the results using this proposed technique are compared with the results obtained using flower pollination algorithm, moth flame optimisation, particle swarm optimisation and evolutionary programming techniques to determine the efficiency of the proposed approach in solving economic dispatch. The simulation results show the capability of WDO in determining the optimal power generation value with minimum generation cost and low rate of power loss.

Different Variants of Particle Swarm Optimization Algorithms and Its Application: A Review

Abstract: Particle Swarm Optimization (PSO) algorithm is an evolutionary development in the field of Artificial Intelligence and computational technique. This technique has grasped the attention of various researchers and scholars in a very short span in comparison to other old techniques, such as—Genetic Algorithm, Evolutionary programming, etc. existing since the 1960s or even before. The review chapter focuses on the brief information about how PSO has evolved since 1995 and the perception about the motivation behind the development of the PSO algorithm. The early modifications to the original form of the algorithm and alteration in the equation are also discussed along with the different types of PSO(s) and their hybrids. Also, the chapter gives an insight into various applications of the PSO and some of the application specified hybrid (or modified) PSO techniques. Based on the state-of-art, discussion on performance comparison of the hybrid (or modified) PSO is also presented in this chapter. It is concluded that the application of objective specific is more fruitful as compared to the implementation of standalone PSO. This chapter could present an interesting brief insight into the researchers working in the field of PSO.

Hyper-heuristic approach: automatically designing adaptive mutation operators for evolutionary programming

Abstract: Genetic programming (GP) automatically designs programs. Evolutionary programming (EP) is a real-valued global optimisation method. EP uses a probability distribution as a mutation operator, such as Gaussian, Cauchy, or Levy distribution. This study proposes a hyper-heuristic approach that employs GP to automatically design different mutation operators for EP. At each generation, the EP algorithm can adaptively explore the search space according to historical information. The experimental results demonstrate that the EP with adaptive mutation operators, designed by the proposed hyper-heuristics, exhibits improved performance over other EP versions (both manually and automatically designed). Many researchers in evolutionary computation advocate adaptive search operators (which do adapt over time) over non-adaptive operators (which do not alter over time). The core motive of this study is that we can automatically design adaptive mutation operators that outperform automatically designed non-adaptive mutation operators.

Design of optimal multi-objective-based facts component with proportional-integral-derivative controller using swarm optimization approach

Abstract: This study proposes a multi-objective-based swarm intelligence method to improve angle stability. An optimization operation with single objective function only improves the performance of one perspective and ignores the other. The combination of two objective functions which derived from real and imaginary components of eigenvalue are able to provide better performance beyond the optimization capabilities of single objective function. Tested using MATLAB, the simulation is performed using a single machine attached to the infinite bus (SMIB) system equipped with static var compensator (SVC) that attached with PID controller (SVC-PID). The objective of this experiment is to explore the excellent parameters in SVC-PID to produce a more stable system. In addition to the comparison of objective functions, this study also compares particle swarm optimization (PSO) capabilities with evolutionary programming (EP) and artificial immune system (AIS) techniques.

Convergence Analysis of Self-Adaptive Equalizers Using Evolutionary Programming (EP) and Least Mean Square (LMS)

Abstract: Digital communication has become an important part of our lives, and technology has been undergoing advancements With the arrival of the age of digitalization and digital signal, communication has got implemented in a vibrant range of applications but still, they are strongly affected by two basic problems, namely Noise and Inter-Symbol Interference (ISI) This is caused by the error-creating phenomena which are characteristics between the transmitter and receiver which include the scattering of the transmitted signal The noise produced in the communication channel is caused by channel characteristics and can be reduced with proper channel selection The SNR can be improved by improving the transmitter signal strength even in spite of noisy signal at the receiver By using the adaptive equalization in channels will reduce this effect drastically and can be implemented by using various adaptive algorithms Hence, an adaptive channel equalizer is used to inverse the effect channel had on the signal to get back the initial information There are many adaptive algorithms to update the coefficients of equalizers; evolutionary algorithms are used in this paper to do so The two algorithms used before are Artificial Bee Colony algorithm (ABC) and Ant Colony Optimization (ACO) The latest algorithm is the combination of Evolutionary Programming and LMS algorithm (EPLMS); this gives better solution faster A comparative study between the algorithms is done in this paper

Multi-Heuristic Based Technique in Multi-SVCs Installation Scheme for Loss Control in Transmission System

Abstract: Increasing demand of power system has been identified as one of the dominant factors in under-voltage phenomenon, instability condition and increment of transmission loss in power system. To alleviate this condition for the purpose of maintaining secure delivery of electricity, appropriate remedial actions are mandatory to be performed in either real time or offline studies. Amongst the popular remedial actions to avoid the loss control is the installation of static VAR compensator (SVC) scheme. This scheme requires a robust optimization technique which should be able to achieve optimal solution with less computation burden. This paper presents the application of multi-heuristic techniques for optimizing the sizing and locations of the SVC installation. Two optimization techniques are proposed in this study; namely the evolutionary programming (EP) and artificial immune system (AIS) for the purpose of optimal determination of locations and sizing of SVCs. Several cases are considered, based on number of units for the installation process; solved under different loading conditions subjected to the system. A reliability test system (RTS) model was utilized as the test specimen in this study; which ultimately highlights the merit of the optimization techniques.

Reactive Power Optimization of Distribution Network with Distributed Generators by Improved Evolutionary Programming Algorithm

Abstract: In the distribution network containing distributed generators (DG), both DG and capacitors can be adjusted for reactive power, and there is a strong complementarity between the two. If the reactive power compensation ability of each grid-connected DG can be fully utilized, it will effectively reduce the voltage fluctuation and the number of equipment actions, which will help to improve the operation level of the distribution network. In this paper reactive power optimization of distribution networks containing distributed power sources has studies. An improved evolutionary programming (EP) method is proposed to solve the optimization goal, using a variable step-size evolution method and optimization strategy with small population and high convergence accuracy. The algorithm improves the random dynamic step method. In different stages of optimization, different step sizes are used to improve the optimization effect. At the same time, considering that some wind farms are equipped with AVC and some wind farms are equipped with capacitor banks, the evolutionary operator is improved. The operator evolves directly on the integer, so it is easier to find the global optimal solution. This paper has used IEEE33 bus system to verification. Results show that the DG can reduce active power loss in distribution network and ameliorate the voltage level. Meanwhile, the improved evolutionary programming method has good optimization effect, fast convergence speed and high search efficiency.

Evolutionary programming and multi-verse optimization based Technique for risk-based voltage stability control

Abstract: Power system these days appears to work at high-stress load, which could trigger voltage security problems. This is due to the fact that the system will operate under low voltage conditions, which could be possibly below the allowable voltage limit. The voltage collapse phenomenon can become one of the remarkable issues in the power systems which can lead to severe consequences of voltage instability. This paper proposes a method for managing the voltage stability risk using two methods which are evolutionary programming (EP) and multiverse optimization (MVO). Consequently, EP and MVO were used to manage the risk in the power system due to load variations. The risk assessment is made in order to determine the risk of collapse for the system utilizing a pre-developed voltage stability index termed as Fast Voltage Stability Index (FVSI). It is used as the indicator of voltage stability conditions. Results obtained from the study revealed that the MVO technique is much more effective compared to EP.

An improved flower pollination solution for economic dispatch with valve point effect

Abstract: The economic dispatch is used to find the best optimal output of power generation at the lowest operating cost of each generator, to fulfill the requirements of the consumer. To get a practical solution, several constraints have to be considered, like transmission losses, the valve point effect, prohibited operating region, and emissions. In this research, the valve point effect is to be considered which increases the complexity of the problem due to its ripple effect on the fuel cost curve. Economic load dispatch problems are well-known optimization problems. Many classical and meta-heuristic techniques have been used to get better solutions. However, there is still room for improvement to get an optimal solution for the economic dispatch problem. In this paper, an Improved Flower Pollination Algorithm with dynamic switch probability and crossover operator is proposed to solve these complex optimization problems. The performance of our proposed technique is analyzed against fast evolutionary programming (FEP), modified fast evolutionary programming (MFEP), improved fast evolutionary programming (IFEP), artificial bee colony algorithm (ABC), modified particle swarm optimization (MPSO) and standard flower pollination algorithm (SFPA) using three generator units and thirteen thermal power generation units, by including the effects of valve point loading unit and without adding it. The proposed technique has outperformed other methods in terms of the lowest operating fuel cost.

Mutation Strategy Based on Step Size and Survival Rate for Evolutionary Programming

Abstract: Evolutionary programming (EP) uses a mutation as a unique operator. Gaussian, Cauchy, Levy, and double exponential probability distributions and single-point mutation were nominated as mutation operators. Many mutation strategies have been proposed over the last two decades. The most recent EP variant was proposed using a step-size-based self-adaptive mutation operator. In SSEP, the mutation type with its parameters is selected based on the step size, which differs from generation to generation. Several principles for choosing proper parameters have been proposed; however, SSEP still has limitations and does not display outstanding performance on some benchmark functions. In this work, we proposed a novel mutation strategy based on both the “step size” and “survival rate” for EP (SSMSEP). SSMSEP-1 and SSMSEP-2 are two variants of SSMSEP, which use “survival rate” or “step size” separately. Our proposed method can select appropriate mutation operators and update parameters for mutation operators according to diverse landscapes during the evolutionary process. Compared with SSMSEP-1, SSMSEP-2, SSEP, and other EP variants, the SSMSEP demonstrates its robustness and stable performance on most benchmark functions tested.

Multi-Objective Immune-Commensal-Evolutionary Programming for Total Production Cost and Total System Loss Minimization via Integrated Economic Dispatch and Distributed Generation Installation

Abstract: Economic Dispatch (ED) problems have been solved using single-objective optimization for so long, as Grid System Operators (GSOs) previously only focused on minimizing the total production cost. In modern power systems, GSOs require not only optimizing the total production cost but also, at the same time, optimizing other important objectives, such as the total emissions of the greenhouse gasses, total system loss and voltage stability. This requires a suitable multi-objective optimization approach in ensuring the ED solution produced is satisfying all the objectives. This paper presents a new multi-objective optimization technique termed Multi-Objective Immune-Commensal-Evolutionary Programming (MOICEP) for minimizing the total production cost and total system loss via integrated Economic Dispatch and Distributed Generation installation (ED-DG). This involved the application of a weighted-sum multi-objective approach that combined with an optimization technique called Immune-Commensal-Evolutionary Programming (ICEP). The proposed MOICEP has been compared with other multi-objective techniques, which are Multi-Objective-Evolutionary Programming (MOEP) and Multi-Objective-Artificial Immune System (MOAIS). It was found that MOICEP performs very well in producing better optimization results for all the three types of Economic Dispatch (ED) problems compared to MOEP and MOAIS in terms of cheap total production costs and low total system loss.

Comparative studies between ant lion optimizer and evolutionary programming in optimal distributed generation placement

Abstract: Integration of Distributed Generation (DG) has become one of the popular research interests in power system. DG used small-scale technologies to generate electricity near to the consumer and the size is normally small, range from 50 MW to 100 MW. However, in order to integrate DG into the power system distribution, it is very crucial to consider several factors such as location, size and the number of DG to maintain its benefits. This paper proposes comparison of three techniques for optimal placement of DG in distribution system. The optimal placement was done using Evolutionary Programming (EP), Ant Lion Optimizer (ALO) and Loss Sensitivity Factor (LSF) to minimize the power losses in distribution system. These techniques were tested in three conditions; base case (without loading increment), 50% loading and 100% loading to observe the performance of the techniques in various phenomena. The test was conducted using IEEE 10-bus radial distribution system and the result shows that the integration of DG does minimize the power losses with ALO provides the most promising results.

Epigenetic opportunities for Evolutionary Computation.

Abstract: Evolutionary Computation is a group of biologically inspired algorithms used to solve complex optimisation problems. It can be split into Evolutionary Algorithms, which take inspiration from genetic inheritance, and Swarm Intelligence algorithms, that take inspiration from cultural inheritance. However, recent developments have focused on computational or mathematical adaptions, leaving their biological roots behind. This has left much of the modern evolutionary literature relatively unexplored. To understand which evolutionary mechanisms have been considered, and which have been overlooked, this paper breaks down successful bio-inspired algorithms under a contemporary biological framework based on the Extended Evolutionary Synthesis, an extension of the classical, genetics focussed, Modern Synthesis. The analysis shows that Darwinism and the Modern Synthesis have been incorporated into Evolutionary Computation but that the Extended Evolutionary Synthesis has been broadly ignored beyond:cultural inheritance, incorporated in the sub-set of Swarm Intelligence algorithms, evolvability, through CMA-ES, and multilevel selection, through Multi-Level Selection Genetic Algorithm. The framework shows a missing gap in epigenetic inheritance for Evolutionary Computation, despite being a key building block in modern interpretations of how evolution occurs. Epigenetic inheritance can explain fast adaptation, without changes in an individual's genotype, by allowing biological organisms to self-adapt quickly to environmental cues, which, increases the speed of convergence while maintaining stability in changing environments. This leaves a diverse range of biologically inspired mechanisms as low hanging fruit that should be explored further within Evolutionary Computation.

A crossover that matches diverse parents together in evolutionary algorithms

Abstract: Crossover and mutation are the two main operators that lead to new solutions in evolutionary approaches. In this article, a new method of performing the crossover phase is presented. The problem of choice is evolutionary decision tree construction. The method aims at finding such individuals that together complement each other. Hence we say that they are diversely specialized. We propose the way of calculating the so-called complementary fitness. In several empirical experiments, we evaluate the efficacy of the method proposed in four variants and compare it to a fitness-rank-based approach. One variant emerges clearly as the best approach, whereas the remaining ones are below the baseline.

A Study of Direct and Indirect Encoding in Phenotype-Genotype Relationships.

Abstract: This paper examines phenotype and genotype mappings that are biologically inspired. These types of coding are used in evolutionary computation. Direct and indirect encoding are studied. The determination of genotype and phenotype relationships and the connection to genetic algorithms, evolutionary programming and biology are examined in the light of newer advances. The NEAT and HyperNEAT algorithms are applied to the 2D Walker [41] problem of an agent learning how to walk. Results and findings are discussed, and conclusions are given. Indirect coding did not improve the situation. This paper shows that indirect coding is not useful in every situation.

Hybrid CEPIDE Optimization for Economic Load Dispatch and Estimation of Transmission Parameters

Abstract: In this paper, classical biological process programming i.e. classical evolutionary programming (CEP) and improved differential evolution (IDE) are utilized to obtain ELD results for 6 unit IEEE system. A chaotic remodelling issue is additionally enclosed in EP rule to ameliorate the concurrence characteristic for the 6-unit IEEE test suit system. The results obtained are satisfactory and within the optimum emission atmosphere. EP-based CEED downside has been tested on IEEE 6-bus system with and without transmission losses. Investigations showed that classical biological process programming and quick biological process programming i.e. fast evolutionary programming (FEP) were higher among biological process computation ways in ascertaining the ELD downside with challenges posed for large thermal units of big thermal power plants. To beat these challenges in large thermal units the projected classical biological process programming based mostly improved differential evolution (CEPIDE) strategy is applied involving a cubic form price, emission and combined objective approach, to satisfy the non-smooth and non-differentiable fuel functions threats of large units to yield higher ends up in comparison to alternative heuristic ways like particle swarm optimization (PSO), differential evolution(DE),Fuzzy and genetic rule etc. The power outputs of classical EP technique are used as initial approximations for IDE technique.

An evolutionary programming-based tabu search method for solving multi area unit commitment problem with import and export constraints

Abstract: This paper presents a new approach to solve the multi area unit commitment problem (MAUCP) using an evolutionary programming-based tabu search (EPTS) method. The objective of this paper is to determine the optimal or a near optimal commitment schedule for generating units located in multiple areas that are interconnected via tie- lines. The evolutionary programming-based tabu search method is used to solve multi area unit commitment problem, allocated generation for each area and find the operating cost of generation for each hour. Joint operation of generation resources can result in significant operational cost savings. Power transfer between the areas through the tie- lines depends upon the operating cost of generation at each hour and tie- line transfer limits. The tie -line transfer limits were considered as a set of constraints during optimization process to ensure the system security and reliability. The overall algorithm can be implemented on an IBM PC, which can process a fairly large system in a reasonable period of time. Case study of four areas with different load pattern each containing 26 units connected via tie- lines has been taken for analysis. Numerical results showed comparing the operating cost using evolutionary programming-based tabu search method with conventional dynamic programming (DP), evolutionary programming (EP), Partical Swarm Optimization (PSO), Simulated Annealing (SA), Evolutionary Programming based Partical Swarm Optimization (EPPSO), Evolu