MOIA: Multi-objective immune algorithm

Luh, Guan-Chun; Chueh, Chung-Huei; Liu, Weiwen

doi:10.1080/0305215031000091578

Cited by 94 publications

(29 citation statements)

References 41 publications

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“…Select P antibodies from A(it) to constitute the memory units M(it) ={m 1 (it),m 2 (it),…,m P (it)}. Step 2 Carry out the clone operation to A(it)∪M(it), and get Z(it): Z(it)= T c (A(it)∪M(it)) ={ T c (a 1 (it)), T c (a 2 (it)),…, T c (a N (it)), T c (m 1 (it)), T c (m 2 (it)),…, T c (m P (it))} ={a 11 (it),…,a 1R (it),…,a N1 (it), …,a NR (it),m 11 (it),…, m 1R (it), …,m P1 (it), …,m PR (it)} ={z 1 (it),z 2 (it), …,z r (it)}, where r=(N+P)R.…”

Section: Specific Steps Of the Performance Optimization Algorithmmentioning

confidence: 99%

“…Artificial immune system algorithm is used to solve multi-objective optimization problem for the first time in 2002 [10]. Then many multi-objective optimization algorithms based on artificial immune system are proposed, such as MOIA [11], VIS [12][13][14], AMOBNS [15] and so on. In recent years, multi-agent system [16,17], quantum computation [18], estimation of distribution algorithm [19], fitness sharing [20], co-evolution theory [21] and differential evolution [22] are also introduced into artificial immune system algorithm.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Performance optimization algorithm of radar signal processing system

2016

Cluster Comput

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A performance optimization algorithm of radar signal processing system is proposed. Based on a new modeling mode of radar signal processing system, the values of performance indexes of radar system can be predicted by a performance prediction method during the design phase. Then based on immune clone and immune memory theory in artificial immune system, this paper presents an immune memory clone constrained multi-objective optimization algorithm to generate the optimal system design schemes automatically. Experimental results show that the proposed performance optimization algorithm can search out the optimal design schemes that make system performance reach or near to optimum. The optimal solutions of the proposed algorithm are more diverse and better in terms of uniformity than NSGA-II and SPEA2.

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Section: Specific Steps Of the Performance Optimization Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Performance optimization algorithm of radar signal processing system

2016

Cluster Comput

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show abstract

“…De Castro and Von Zuben (2000,2002) used the clonal selection principle to perform machine learning and pattern recognition tasks and to solve optimization problems. Luh et al (2003) proposed an immune based algorithm for finding Pareto optimal solutions to multi-objective optimization problems. Coello Coello and Cortes (2005) applied clonal selection principle to solve multi-objective optimization problems.…”

Section: Previous Work In Artificial Immune Systemmentioning

confidence: 99%

Solving a Multi-Objective No-Wait Flow Shop Problem by a Hybrid Multi-Objective Immune Algorithm

Tavakkoli–Moghaddam¹,

Rahimi-Vahed²,

Mirzaei³

2007

Multiprocessor Scheduling, Theory and Applications

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“…In recent years, AIS has been applied to solving MOPs, and studies show remarkable performances. Luh et al (2003) proposed a novel scheme, named the multi-objective immune algorithm (MOIA), which searches unconstrained Pareto solutions. The results showed that the MOIA generally performs better than SPEA for six test functions and also better than MOGA, NPGA, and NSGA.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Multi-objective Immune Algorithm for Optimal Design of Truss Structures

Xie

Tang

et al. 2016

Journal of Asian Architecture and Building Engineering

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In this paper, an adaptive immune clone selection algorithm for multi-objective optimization (AICSAMO) is proposed. A novel adaptive polynomial mutation operator with dynamic mutation probability is employed in AICSAMO. This adaptive mutation operator executes a rapid global search at the earlier stage of the algorithm and a fine-tuning search at the later stage of the algorithm, which adopts generation-dependent parameters to improve the convergence speed and global optimum searching ability. The effectiveness of AICSAMO is evaluated through the truss sizing and shape optimization problems of a 10-bar plane truss and a 25-bar space truss. According to the comparison of AICSAMO with various multi-objective optimization algorithms developed recently, the simulation results illustrate that AICSAMO has remarkable performance in finding a wider spread of optimal solutions and in maintaining better uniformity of the solutions with better convergence.

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MOIA: Multi-objective immune algorithm

Cited by 94 publications

References 41 publications

Performance optimization algorithm of radar signal processing system

Performance optimization algorithm of radar signal processing system

Solving a Multi-Objective No-Wait Flow Shop Problem by a Hybrid Multi-Objective Immune Algorithm

An Adaptive Multi-objective Immune Algorithm for Optimal Design of Truss Structures

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